NASA CMS Project
atmosphere carbon climate cog earth observation elevation geospatial hydrology ice land cover lidar netcdf oceans precipitation radar satellite imagery soil moisture weather
Description
This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.
Salt_Marsh_Biomass_CONUS_2348
This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.Howland_Forest_Biomass_Map_2434
This dataset holds aboveground biomass (AGB) estimates at 10-m spatial resolution for the Howland Research Forest in central Maine for 2012, 2015, 2017, 2021, and 2023. Forest inventory data were collected using 50 fixed-area plot sampling during the summers of 2021, 2023, and 2024. Plots included permanent inventory plots around existing flux towers and additional plots to ensure representation of various forest conditions. Each plot had a radius of 7.98 m. In addition, leaf-off airborne LiDAR data were collected by the USGS 3DEP project in 2012, 2015, and 2023, and leaf-on data were obtained from the NASA G-LiHT project for 2017 and 2021. The LANDIS-II forest landscape model along with its Biomass Succession extension was used to simulate ecosystem dynamics in Howland Forest. Then, a random forest (RF) model was used to generate wall-to-wall biomass maps for the research forest from the LiDAR data. The RF model was calibrated from in situ AGB measurements from plots and simulated AGB values for the LiDAR acquisition years. Howland Research Forest is a low-elevation transitional forest dominated by spruce and hemlock, with conifer and northern hardwood species. The data are provided in cloud optimized GeoTIFF format.Tidal_Marsh_Biomass_US_V1-1_1879
This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%.CMS_AGB_Landcover_Indonesia_1645
This dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.CMS_iWED_V1_2452
This dataset provides an integrated Wildfire Event Dataset (iWED version 1) for wildfire events of 100 ha or more in area from 1992 to 2021 for the continental US. Fire information was compiled from a variety of state, regional, and federal-level agencies responsible for filing and archiving incident level reports. Additional information was obtained from the Monitoring Trends in Burn Severity (MTBS) program initiated in the mid-2000s. MTBS is being continuously updated as new satellite remote sensing data are collected and processed. The data are provided in comma separated values (CSV) format.CMS_AGB_NW_USA_1719
This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.Annual_Burned_Area_Maps_1708
This dataset provides maps of annual burned area for the part of Mawas conservation program in Central Kalimantan, Indonesia from 1997 through 2015. Landsat imagery (TM, ETM+, OLI/TIR) at 30 m resolution was obtained for this 19-year period, including the variables surface reflectance, brightness temperature, and pixel quality assurance, plus the indices NDVI, NDMI, NBR, NBR2, SAVI, and MSAVI. The MODIS active fire product (MCD14) was used to define when fires occurred. Random Forest classifications were used to separate burned and unburned 30-m pixels with inputs of composites of Landsat indices and thermal bands, based on the pre- and post-fire values.CMS_WFEIS_CONUS-AK_1306
This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates.Blue_Carbon_Tidal_Wetland_Maps_2091
This dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production. The maps include current dominant wetland type, restoration category, potential removal of atmospheric greenhouse gases in units of mass carbon dioxide with estimates of uncertainty.BlueFlux_AirborneObs_Florida_2327
This dataset includes airborne in situ measurements of greenhouse gas mixing ratios, meteorological parameters, and fluxes (CO2, CH4, latent heat fluxes, friction velocity, and convective velocity scale) calculated with wavelet transforms. CO2, CH4, CO, O3, and water vapor mixing ratios, and meteorological variables were obtained from a Beechcraft A90 King Air aircraft. Flights occurred on April 19-26 2022, October 14-20 2022, February 5-13 2023, and April 13-19 2023 as part of the BlueFlux campaign, funded by NASA's Carbon Monitoring System program. Measurements were made with several instruments, including a PICARRO 2401-m (0.5 Hz CO2/CH4/H2O/CO), PICARRO 2311-f (10 Hz CO2/CH4/H2O), NASA Rapid Ozone Experiment (ROZE, 10 Hz O3), and AIMMS-20 probe (3-D winds, meteorology, and aircraft location data). Flight lines span Everglades National Park (ENP) and Big Cypress National Preserve (BCNP) in southern Florida, USA. The measurements were used to calculate vertical fluxes of trace gases and heat via wavelet transform eddy covarianceBlueFlux_Tidal_River_Water_2333
This dataset provides dissolved carbon (dissolved inorganic carbon and dissolved organic carbon), greenhouse gases, dissolved organic matter optical, and hydrological (water temperature, pH, alkalinity, dissolved oxygen) data collected from the Shark and Harney tidal rivers in the Everglades, Florida, USA. The data were collected as part of the NASA Carbon Monitoring System (CMS) BlueFlux field campaigns over the 2022 wet season (October 2022) and 2023 dry season (March 2023). Data includes single-collection samples collected from sites along both rivers and samples collected by an autosampler at one site over multiple tidal cycles. The data are provided in comma-separated values (.csv) format.BlueFlux_Gridded_CO2_CH4_2404
This dataset contains gridded estimates of carbon dioxide (CO2) and methane (CH4) fluxes at daily resolution covering the Southern Florida region from 2000 to 2024. Gridded CO2 and CH4 flux prototype products at 500-m spatial resolution were derived from a machine learning model based on eddy covariance (EC) measurements from 1) airborne fluxes collected seasonally with the NASA Carbon Airborne Flux Experiment (CARAFE) over the region during five flight deployments and 2) regional EC tower networks representing long term wetland ecosystem fluxes since 2004. Multiscale flux measurements were upscaled with remote sensing observations of MODIS optical reflectance using a bootstrap ensemble random forest modeling approach to predict daily mean flux intensity and uncertainty from February 2000 to August 2024. Prototypes of modeled, gridded greenhouse gas fluxes were developed as part of the NASA Carbon Monitoring System (CMS) BlueFlux Project. The data are provided in netCDF format.TLS_Lidar_BlueFlux_Mangroves_2311
This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS "Blueflux" campaigns in March 2022, October 2022, and March 2023. Products were acquired using a RIEGL VZ-400i terrestrial laser scanner (TLS). TLS is a non-destructive and quantitative method for in situ 3D forest structure measuring and monitoring. Data are provided in LAS (.las) format.Boreal_Arctic_Wetland_CH4_2351
This dataset provides an upscaled estimate of Boreal-Arctic wetland CH4 emissions at a weekly time scale from 2002 to 2021 at 0.5 by 0.5-degree spatial resolution. Ground truth data on wetland CH4 emissions from eddy covariance towers (139 site years) and chambers (168 site years) were used to train and validate a causality-guided machine learning model. The trained model was then used to estimate CH4 emissions at grid cells that have wetlands and located above 44 degrees north. The data are provided in netCDF format.CMSFluxFire
This dataset provides the Carbon Flux for Fires. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxFossilFuelPrior
This dataset provides the Carbon Flux for Fossil Fuel Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxOceanPrior
This dataset provides the Carbon Flux for Ocean Carbon Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxFossilFuelPrior
This dataset provides the Prior for the Fossil Fuel Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxNBE
This dataset provides the Carbon Flux from the Net Biome Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxNBEPrior
This dataset provides the Carbon Flux from the Net Biome Exchange Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxLandPrior
This dataset provides the Prior for the Land Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxNBE
This dataset provides the Carbon Flux for Posterior Net Biome Exchange (NBE). The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxOcean
This dataset provides the Posterior Carbon Flux for the Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxOceanPrior
This dataset provides the Prior for the Carbon Flux for Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxTotal
This dataset provides the Carbon Flux for Posterior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxTotalPrior
This dataset provides the Prior for Total Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxFossilfuel
This dataset provides the Carbon Flux for Fossil Fuel. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxOcean
This dataset provides the Carbon Flux for Ocean Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxTotalprior
This dataset provides the Carbon Flux for Prior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxMISC
This dataset provides the Carbon Flux for Shipping, Aviation, and Chemical Sources. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSFluxNEE
This dataset provides the Carbon Flux from the Net Ecosystem Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSLakeHuronPPM
Monthly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSLakeHuronPPY
Yearly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSLakeMichiganPPM
Monthly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSLakeMichiganPPY
Yearly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSLakeSuperiorPPM
Monthly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMSLakeSuperiorPPY
Yearly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_CONUS_Biomass_1752
This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model. Measurements of pixel-specific site conditions from remote sensing data were combined with field inventory data from the U.S. Forest Service Forest Inventory and Analysis (FIA). Remote sensing data inputs included Thematic Mapper on Landsat 5, Operational Land Imager on Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, microwave radar measurements from Phased Array type L-band Synthetic Aperture Radar (PALSAR) on Advanced Land Observation Satellite (ALOS) and PALSAR-2 ALOS-2, airborne imagery from National Agriculture Imagery Program (NAIP), and the digital elevation model from the Shuttle Radar Topography Mission (SRTM). Data from satellite and airborne sources were co-registered on a common 100 m (1 ha) grid.CMS_CTL_NA_GOSAT_FOOTPRINTS
This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the GOSAT satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.CMS_CTL_NA_OCO2_FOOTPRINTS
This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 14 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.CMS_CTL_NA_TCCON_FOOTPRINTS
This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the TCCON ground network. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.CMS_Global_Forest_Age_2345
This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.CMS_Simulated_SIF_NiwotRidge_1720
This dataset provides results for simulations of solar-induced chlorophyll fluorescence (SIF) implemented within the terrestrial biosphere Community Land Model (CLM 4.5) for Niwot Ridge, Colorado, USA, from 1998-2018. The data include outputs from three model simulations designed to test the importance of non-photochemical quenching (NPQ), that is, the absorbed light energy dissipated as heat, in determining seasonal SIF.C_FluxStocks_CLM5_DART_WestUS_1856
This dataset provides monthly estimates of biomass stocks and land-atmosphere carbon exchange across the western United States at 0.95 degrees longitude x 1.25 degrees latitude grid resolution from 1998 through 2010. The data include outputs from two types of model simulations: (1) a "free" simulation which used Community Land Model (CLM5.0) simulations forced with meteorology appropriate for complex mountainous terrain, and (2) "assimilation" runs using the land surface data assimilation system (CLM5-DART). In assimilation runs, the CLM5 vegetation state is constrained by remotely sensed observations of leaf area index and aboveground biomass, which influenced biomass stocks and carbon fluxes.CMS_GO_CH4_SEC_TDYC_NA
Methane emissions are provided by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), En- vironment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM).CMS_FluxEstimates_Aircraft_CO2_2336
This dataset provides gridded surface-atmosphere CO2 fluxes over North America from April 8 to November 18 during 2018 and 2019. Net ecosystem exchange (NEE) was estimated by the CMS-Flux-NA CO2 inversion system by assimilating in situ CO2 measurements and/or Orbiting Carbon Observatory (OCO-2) column-averaged CO2 retrievals. These data, along with imposed diurnal NEE variations, fossil fuel emissions, biomass burning, and biofuel emissions, are provided at 3-hour temporal resolution. The modeled co-samples of CO2 observed for aircraft flights are included for model evaluation. The data are provided in NetCDF version 4 format.CMS_Mangrove_Biomass_Zambezi_1522
This dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013. Aboveground biomass estimates for the larger area of mangrove coverage within the delta are based on (1) the plot level data and (2) canopy structure data derived from airborne LiDAR surveys in 2014. The high-resolution canopy height model for the delta region derived from the airborne LiDAR data is also included.CMS_LiDAR_AGB_PEF_2012_1318
This data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF. The estimates were produced through a novel modeling approach that accommodates temporal misalignment between field measurements and remotely sensed data by including multiple time-indexed measurements at plot locations to estimate changes in AGB.Global_Riverine_N2O_Emissions_1791
This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order). The estimates were derived from a water transport model, the Model for Scale Adaptive River Transport (MOSART), coupled with the Dynamic Land Ecosystem Model (DLEM) to link hydrology and ecosystem processes pertaining to N2O flux and transport. Factors driving the model included climate, land use and land cover, and nitrogen inputs (i.e., fertilizer, deposition, manure, and sewage). Nitrogen discharges from streams and rivers to the ocean were calibrated from observations from 50 river basins across the globe.Atmospheric_CO2_California_1641
This dataset provides measurements of atmospheric CO2 concentrations, carbon isotopes d13C and D14C, and fossil fuel CO2 (ffCO2) estimates from nine observation sites in California over three month-long campaigns in separate seasons of 2014-2015. ffCO2 was quantified based on the CO2 concentration and D14C. Simulations of ffCO2 at the sites and times of the observations were conducted with the Vulcan v2.2 fossil fuel emissions estimate for 2002 and the Weather Research and Forecasting - Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) atmospheric model. The observed and simulated ffCO2 were incorporated into Bayesian inverse estimates of ffCO2 to calculate California's ffCO2 emissions during the campaign period.CMS_Methane_Emissions_Boston_1291
This data set provides average hourly measured, modeled enhancements, and background methane (CH4) concentrations, atmospheric ethane (C2H6) measurements, prior CH4 flux fields by sector, and a spatial reconstruction of natural gas (NG) consumption in Boston, Massachusetts and the surrounding region. Atmospheric CH4 concentrations were measured continuously from September 2012 through August 2013 at four locations and atmospheric ethane was measured continuously for several months during 2012-2014 at one location. Spatial models of prior CH4 emissions and natural gas consumption are given for an ~18,000 km^2 area centered on Boston, MA.CMS_Global_Cropland_Carbon_1279
This data set provides global estimates of carbon fluxes associate with annual crop net primary production (NPP) and harvested biomass, annual uptake and release by humans and livestock, and the total annual estimate of net carbon exchange (NCE) derived from these carbon fluxes. NCE estimates are for the combined crop plant harvest and consumption/expiration of fodder by livestock and of food by humans. Estimation of carbon uptake and release from global agricultural production and consumption required compilation and analysis of inventory data from various sources for the years 2005-2011. The flux estimates were spatially distributed to a global 0.05-degree resolution grid using MODIS land cover data. The quantities of carbon flux in each gridcell are represented in two ways: (1) where the quantities of carbon distributed to each gridcell were divided by the total gridcell area, resulting in average carbon fluxes per unit of total area (g C/m2/yr), and (2), where annual carbon fluxes associated with a source were summed over all types for the gridcell (Mg C/yr). The total surface area of the grid cells is provided. There are eight data files in netCDF format (.nc4) with this data set -- two files (per area and per gridcell) for each of the four flux source types. Data for all years are in each .nc4 file.CMS_WRF_Footprints_CO2_Signals_1381
This data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes. Data is also provided for the land surface in the larger modeling domain outside California. CO2 signals refer to the local enhancement or depletion in atmospheric CO2 concentration caused by fossil fuel emissions or biospheric exchange occurring within the region.GPP_CONUS_TROPOMI_1875
This dataset includes estimates of gross primary production (GPP) for the conterminous U.S., for 2018-02-15 to 2021-10-15, based on measurements of solar-induced chlorophyll fluorescence from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite platform. GPP was estimated from rates of photosynthesis inferred from SIF using a linear model and ecosystem scaling factors from 102 AmeriFlux sites. Knowledge of the spatiotemporal patterns of GPP is necessary for understanding regional and global carbon budgets. Broad-scale estimates of GPP have typically relied upon carbon cycle models linking spatial patterns of vegetation with remotely sensed environmental data. SIF provides a means to directly estimate photosynthetic activity, and therefore, GPP. Recent deployments of satellite platforms that measure SIF provide near-real-time measurements and represent a breakthrough in measuring GPP on a global scale. Regular SIF measurements can detect spatially explicit ecosystem-level responses to climate events such as drought and flooding. This dataset includes spatially explicit estimates of GPP (g m-2 d-1), uncertainty in GPP, and related TROPOMI SIF measurements (mW m-2 sr-1 nm-1) at 500-m resolution. The data are provided in NetCDF format.CMS_Pantropical_Forest_Biomass_1337
This data set provides estimates of pre-deforestation aboveground live woody biomass (AGLB) at 30-m resolution for deforested areas of tropical America, tropical Africa, and tropical Asia for the year 2000. The biomass estimates are only for areas where deforestation occurred during the period 2000 through 2012. These estimates represent biomass loss over this time period and can be used to derive average annual carbon emissions from tropical deforestation.CMS_Daily_ET_MexFlux_1309
This data set provides daily average observations for evapotranspiration (measured and gap-filled), precipitation, net radiation, soil water content, air temperature, vapor pressure deficit, and normalized vegetation index (NDVI) from two water-limited shrubland sites for years 2008-2010. Both sites are located in the northwest part of Mexico and are part of the MexFlux network.CMS_Fire_Weather_Data_AK_1509
This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.FIA_Forest_Biomass_Estimates_1873
This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S. This dataset, derived from field survey data from 2009-2019, includes statistical estimates of biomass at the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. Estimates include the mean (and standard error of the mean) biomass for both live and dead trees, calculated using three sets of allometric equations. There is also an estimate of the area of forestland in each hexagon. These data can be useful for assessing the accuracy of remotely sensed biomass estimates.CMS_Forest_Productivity_1221
Notice: This data set and guide were updated on June 30, 2014 to correct an error in the reported units. The data values were not changed.Spatially-gridded estimates of above ground biomass (AGB), net primary productivity (NPP), and net ecosystem productivity (NEP) are provided for forested areas of the conterminous United States (CONUS). Estimates of uncertainty are also provided for AGB and NEP. These data were derived by using Forest Inventory and Analysis (FIA) data to constrain forest growth rates in a Carnegie-Ames-Stanford Approach (CASA) carbon-cycle process model. Note that the data set does not include data for forests in the Northern Prairie States region (NPS; see Figure 3). These data provide a detailed estimate of carbon sources and sinks from recent forest disturbance and recovery across regions and forest types of the US. The data are presented as a series of ten NetCDF v4 (.nc4) files at two spatial scales (1-degree and 5-km spatial resolution) for the nominal year of 2005.CMS_Forest_Carbon_Fluxes_1313
This data set provides maps of estimated carbon in forests of the 48 continental states of the US for the years 2005-2010. Carbon (termed committed carbon) stocks were estimated for forest aboveground biomass, belowground biomass, standing dead stems, and litter for the year 2005. Carbon emissions were estimated from land use conversion to agriculture, insect damage, logging, wind, and weather events in the forests for the years 2006 - 2010. Committed net carbon flux was estimated as the sum of carbon emissions and sequestration. The maps are provided at 100-m spatial resolution in GeoTIFF format. Average annual carbon estimates, by US county, for (1) emissions for the multiple disturbance sources, (2) sequestration, and (3) the committed net carbon flux are provided in an ESRI shapefile.CMS_Landscapes_Brazil_Forests_1301
This data set provides measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories taken at the Fazenda Cauaxi and the Fazenda Nova Neonita, Paragominas municipality, Para, Brazil. Also included for each tree are the common, family, and scientific name, coordinates, canopy position, crown radius, and for dead trees the decomposition status. These biophysical measurements were made at Fazenda Cauaxi during 2012 and 2014 and at the Fazenda Nova Neonita during 2013.LIDAR_FOREST_CANOPY_HEIGHTS_1271
This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates. Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute. Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country.CMS_Global_Monthly_Wetland_CH4_1502
This data set provides global monthly wetland methane (CH4) emissions and uncertainty data products derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies. The data are at 0.5 by 0.5-degree resolution. Two model output data products are included in WetCHARTs v1.0: an output from the full ensemble for 2009-2010 and an output from a limited subset for 2001-2015. The intended use of the products is as a process-informed wetland CH4 emission and uncertainty data set for atmospheric chemistry and transport modelling (WetCHARTs).MonthlyWetland_CH4_WetCHARTsV2_2346
This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.3 is an updated product of WetCHARTs v1.3.1 dataset. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members. The data are provided in netCDF format.CMS_Global_Livestock_CH4_CO2_1329
This data set provides global annual carbon flux estimates, at 0.05-degree resolution, associated with livestock feed intake, manure, manure management, respiration, and enteric fermentation, summed over all livestock types. These fluxes can be summed across multiple grid cells to obtain totals for any given areas. These 2000-2013 flux estimates were based on livestock populations reported by the Food and Agriculture Organization (FAO) and the United States Department of Agriculture National Agricultural Statistics Service (USDA NASS), on coefficients provided by the Intergovernmental Panel on Climate Change (IPCC), and on additional coefficients developed by the authors.CMS_Global_Mangrove_Forest_Ht_2251
This dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models. Also provided are Lidar estimates of canopy height based on the GEDI instrument, which were used for training and validation of the TanDEM-X estimates of forest height. The coverage of these data follows Global Mangrove Watch's mangrove extent maps. These spatially explicit maps of mangrove canopy height can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (>60 m) that surpass maximum heights of other forest types. Maps are provided in cloud optimized GeoTIFF format, and mangrove heights for individual GEDI tiles are compiled in a comma separated values (CSV) files.CMS_WRF_Model_Products_1338
This data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system.GCAM_Land_Cover_2005-2095_1216
The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled toLandcover_Colombian_Amazon_1783
This dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data. Annual maps of land cover were created for each Landsat scene and then post-processed and mosaicked. Land cover types include unclassified, forest, natural grasslands, urban, pastures, secondary forest, water, or highly reflective surfaces. The training data are not included with this dataset.Sonoma_County_Forest_AGB_1764
This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated.CMS_Landscapes_Brazil_LiDAR_1302
This data set provides raw LiDAR point cloud data and derived Digital Terrain Models (DTMs) for five forested areas in the municipality of Paragominas, Para, Brazil, for the years 2012, 2013, and 2014. Data are included for two areas in Paragominas for 2013 and 2014, two areas for the Fazenda Cauaxi for 2012 and 2014, and for the Fazenda Andiroba for 2014. Shapefiles showing the LiDAR/DTM coverage areas are also provided for each of the areas.CMS_LiDAR_Indonesia_1518
This dataset provides airborne LiDAR data collected over 90 sites totaling approximately 100,000 hectares of forested land in Kalimantan, Indonesia on the island of Borneo in late 2014. The data were collected as part of an effort to establish a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.CMS_LiDAR_Point_Cloud_Zambezi_1521
This data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.CMS_Maryland_AGB_Canopy_1320
This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements. The field-based estimates were related to LiDAR height and volume metrics through random forests regression models across three physiographic regions of Maryland.CMS_LiDAR_Biomass_CanHt_Sonoma_1523
This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches. Estimates of AGB uncertainty are also provided. Maximum canopy height and tree cover were derived from LiDAR data and high-resolution National Agriculture Imagery Program (NAIP) images.CMS_LiDAR_Products_Indonesia_1540
This dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia. The data were produced as part of an effort to improve a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.CMS_Pilot_Biomass_1257
These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies. These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site). The data are presented as a series of 11 GeoTIFF (.tif) files.CMS_Pennsylvania_Tree_Cover_1334
This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.CMS_Mangrove_CanHt_Stand_Age_1377
This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques. Landsat imagery circa 1990 and circa 2014 was used to estimate stand age between 1994 and 2014 and for forest land cover change modeling.CMS_Mangrove_Canopy_Ht_Zambezi_1357
This data set provides high resolution canopy height estimates for mangrove forests in the Zambezi Delta, Mozambique, Africa. The estimates were derived from three separate canopy height models (CHM) using airborne Lidar data, stereophotogrammetry with WorldView 1 imagery, and Interferometric-Synthetic Aperture Radar (In-SAR) techniques with TanDEM-X imagery. The data cover the period 2011-10-14 to 2014-05-06.CMS_Mangrove_Canopy_Height_1327
This data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.CMS_Mangrove_Cover_1670
This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.CMS_CO2_Fluxes_TBMO_1315
This data set provides global, gridded, model-derived net ecosystem exchange (NEE) of CO2 flux between the land and atmosphere at 3-hourly time steps over seven years (2004-2010) at three different spatial resolutions: 0.5 x 0.5 degree, 2.0 x 2.5 degrees, and 4.0 x 5.0 degrees (latitude/longitude). The 3-hourly data were derived from monthly NEE outputs of 15 global land surface models and four ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP).CMS_SST_GPP_Mexico_1310
This data set provides data for MODIS-derived (1) gross primary productivity (GPP) for the years 2000-2010, (2) fraction of photosynthetically active radiation (fPAR) for the years 2003-2013, (3) sea surface temperature (SST) for the years 2003-2013, and (4) the NOAA-source Multivariate ENSO Index (MEI) data for the years 2003-2013 (as a measure of the El Nino/Southern Oscillation). The study areas were three transects on the Baja California Peninsula, Mexico, and the adjacent Pacific Ocean. The terrestrial transects, in order from North to South, West to East included Punta Colonet (three sites-PC1, PC2, PC3), Punta Abreojos (two sites-PA1, PA2), and Magdalena Bay (three sites-MB1, MB2, MB3).CMS_Great_Basin_Biomass_1755
This dataset provides annual maps of live aboveground tree biomass (Mg/ha) for pinyon-juniper forests across the Great Basin of the Western USA for the years 2000-2016 at a spatial resolution of 30 meters. Biomass estimates are limited to areas of the Great Basin defined as a pinyon-juniper ecosystem type by the 2016 Landfire Existing Vegetation Type map. The estimates of biomass were based on a linear relationship with pinyon-juniper canopy cover and crown-based allometrics developed from field data in Nevada and Idaho. Canopy cover was estimated from remote sensing by using annual composites of Landsat imagery, which were temporally segmented with the LandTrendr algorithm, along with biologically-relevant climate variables, and topographic indices in a Random Forest regression model. Models of canopy cover were trained from semi-automatic extraction of tree crowns from 2011 - 2013 high resolution imagery (1 m) from the National Agriculture Imagery Program, which were validated with photo interpretation. Maps of the standard deviation of biomass estimates from decision trees in the Random Forest model are provided as an indicator of uncertainty. Biomass estimates were calibrated to estimates from the Forest Inventory and Analysis program (FIA) on an annual basis and corrections applied.CMS_SABGOM_Model_Simulations_1510
This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of America simulated by the South Atlantic Bight and Gulf of America (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of America to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.CMS_Soil_CO2_Efflux_1298
This data set provides the results of (1) monthly measurements of soil CO2 efflux, volumetric water content, and temperature, and (2) seasonal measurements of soil (porosity, bulk density, nitrogen (N) and carbon (C) content) and vegetation (leaf area index (LAI), litter and fine root biomass) properties in a water-limited ecosystem in Baja California, Mexico. Measurements and samples were collected from August 2011 to August 2012.C_Pools_Fluxes_CONUS_1837
This dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon. Carbon fluxes include gross primary production (GPP), net primary production (NPP), net biome exchange, autotrophic respiration, and heterotrophic respiration. The modeled estimates are provided as monthly averages over the 16-year period, 2001 through 2016. The data were derived from the CARbon DAta MOdel fraMework (CARDAMOM) that included climate data, and above and below ground biomass maps of CONUS for the years 2005, 2010, 2015 and 2016 as input data sources to this model-data fusion framework. The input data were integrated into the CARDAMOM model to constrain on the terrestrial carbon and to specifically attribute changes of forest carbon stocks and spatial distributions of carbon emissions and removals across forested lands. United States Forest Service's Forest Inventory and Analysis (FIA) plot data were used to train models for the prediction of forest above-ground biomass (AGB).Vermont_HighRes_LandCover_2072
This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.Tree_Canopy_Cover_Mexico_V2_2445
This dataset provides 20-year tree cover (TC) estimates at 30-m spatial resolution for Mexico from 2000 to 2019 using Landsat time series, airborne LiDAR, and machine learning. The TC data (hereafter, CMS-TC) offers accurate and consistent national-scale percent tree cover estimates with an overall coefficient of determination (R squared) of 0.81 and a root mean square error (RMSE) of 11.90%. The CMS-TC product is essential for tracking tree cover and aboveground biomass changes, monitoring land use dynamics, supporting biodiversity conservation, and informing climate and land use policy decisions. The data are provided in GeoTIFF format.Continuous_Lifeform_Maps_CONUS_1809
This dataset contains estimates of percent cover of tree, shrub, herb, and other (non-vegetation) lifeform classes and uncertainties for the conterminous U.S. (CONUS). The estimates were derived using quantile regression forest models and indicate the percent of ground covered by a vertical projection of each lifeform class ranging from 0 to 100 percent. Model input data included Landsat surface reflectance (SR) data and 165 airborne LiDAR datasets covering eight of the eleven terrestrial biomes of the conterminous U.S. and Alaska. Eighty-six of the LiDAR acquisitions are part of the NASA Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) airborne imager data collection; the remaining 79 sites were acquired by the National Science Foundation's National Ecological Observatory Network Airborne Observation Platform (NEON AOP). Acquisitions were selected based on the availability of the SR data for each G-LiHT and NEON dataset. The data are annual estimates from 1984 to 2018 and were tiled (425 tiles) using the CONUS Landsat Analysis Ready Data (ARD) grid scheme. Data are provided in GeoTIFF format.Uncertainty_US_Coastal_GHG_1650
This dataset provides maps of coastal wetland carbon and methane fluxes and coastal wetland surface elevation from 2006 to 2011 at 30 m resolution for coastal wetlands of the conterminous United States. Total coastal wetland carbon flux per year per pixel was calculated by combining maps of wetland type and change with soil, biomass, and methane flux data from a literature review. Uncertainty in carbon flux was estimated from 10,000 iterations of a Monte Carlo analysis. In addition to the uncertainty analysis, this dataset also provides a probabilistic map of the extent of tidal elevation, as well as the geospatial files used to create that surface, and a land cover and land cover change map of the coastal zone from 2006 to 2011 with accompanying estimated median soil, biomass, methane, and total CO2 equivalent annual fluxes, each with reported 95% confidence intervals, at 30 m resolution. Land cover was quantified using the Coastal Change Analysis Program (C-CAP), a Landsat-based land cover mapping product.Niwot_Ridge_CNPAM_Fluorescence_1722
This dataset provides chlorophyll fluorescence measurements made on pine and spruce needle tissues at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Two types of measurements were made using pulse-amplitude-modulation (PAM) fluorometry: the photosystem II (PSII) operating efficiency in the light (Fq'/Fm' at variable light levels), and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) on dark-acclimated tissues. Chlorophyll fluorescence measurements were made to determine seasonality of photosynthetic performance at the needle level.Niwot_Ridge_Pigment_1723
This dataset provides concentrations of pigments in pine and spruce needle tissues collected at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Pigments measured included Chlorophyll A and B, Violaxanthin, Antheraxanthin, Zeaxanthin, Neoxanthin, Lutein, and beta-Carotene. Measurements were made on sun foliage from two canopy-access towers near the main flux tower, and in the laboratory on branches collected from those towers, every 4-8 weeks over the annual cycle. Due to canopy structure, a limited number of trees were accessible from the towers, preventing extensive replication. Pigments were extracted in acetone and analyzed by HPLC. The measurements were made to evaluate seasonal changes associated with the down-regulation of photosynthesis.CMS_DARTE_V2_1735
This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.GCRW_DEM_2016_1793
This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW.Disturbance_Biomass_Maps_1679
This dataset provides derived disturbance history and predicted annual forest biomass maps at 30-m resolution for six selected Landsat scenes across the Conterminous United States (CONUS) for the period 1985-2014. The focus sites are in the following states: Colorado, Maine, Minnesota, Oregon, Pennsylvania, and South Carolina. These scenes were selected to represent a wide range of forest ecosystems, which ensured that a diversity of forest type groups and forest change processes (e.g., harvest, fire, insects, and urbanization) were included. Disturbance history was derived from a Landsat time-series for each site. Each disturbance is represented by year of detection, duration, and magnitude. The cause of the disturbance was not identified. Forest biomass was measured at field plots within each of the six sites and combined with airborne LiDAR data from each site to create land validation maps. Site biomass at 30-m resolution was estimated by developing Random Forest models that include site disturbance history with the land validation maps.Cropland_Carbon_Fluxes_2125
This dataset contains daily estimates of carbon fluxes in croplands derived from the "ecosys" model covering a portion of the Midwestern US (Illinois, Indiana, and Iowa) at county-level resolution from 2001-2018. Ecosys simulates water, energy, carbon, and nutrient cycles simultaneously for various ecosystems, including agricultural systems at up to hourly resolution. Estimates include: gross primary productivity (GPP), net primary productivity (NPP), autotrophic respiration (Ra), heterotrophic respiration (Rh), or net ecosystem exchange (NEE). Data were generated by the ecosys model constrained by observational data, including USDA crop yield from USDA National Agricultural Statistics Service, and a remote-sensing-based SLOPE GPP product. Model performance was evaluated using observations from AmeriFlux towers at agricultural sites within the study area. Agriculture in the US Midwest produces significant quantities of corn and soybeans, which are key elements to the global food supply. The data are provided in shapefile format.EF_Data_Mexico_1693
This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.CMS_EFT_CONUS_1659
This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.DLEM_C_N_Export_1699
This dataset provides estimates for export and leaching of dissolved inorganic carbon (DIC), dissolved organic carbon (DIC), total organic carbon (TOC), particulate organic carbon (POC), ammonium (NH4+), nitrate (NO3-), and total organic nitrogen (TON) from the Mississippi River Basin (MRB) to the Gulf of Mexico. The estimates are provided for a historical period of 1901-2014, and a future period of 2010-2099 (carbon estimates only) under two scenarios of high and low levels of population growth, economy, and energy consumption, respectively. The estimates are from the Dynamic Land Ecosystem Model 2.0 (DLEM 2.0). These data are applicable to studying how changes in multiple environmental factors (e.g., fertilizer application, land-use changes, climate variability, atmospheric CO2 and N deposition) affect the dynamics of leaching and export to the Gulf of Mexico.Fire_Emissions_Indonesia_2118
This dataset provides 10-minute fire emissions within 0.1-degree regularly spaced intervals across Indonesia from July 2015 to December 2020. The dataset was produced with a top-down approach based on fire radiative energy (FRE) and smoke aerosol emission coefficients (Ce) derived from multiple new-generation satellite observations. Specifically, the Ce values of peatland, tropical forest, cropland, or savanna and grassland were derived from fire radiative power (FRP) and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE for each 0.1-degree interval was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution (10 mins) Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution (375 m) VIIRS. This new dataset was named the Fused AHI-VIIRS based fire Emissions (FAVE). Fire emissions data are provided in comma-separated values (CSV) format with one file per month from July 2015 to December 2020. Each file includes variables of fire observation time, fire geographic location, classification, fire radiative energy, various fire emissions and related standard deviations.CMS_Forest_Carbon_Maryland_1660
This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the National Land Cover Database (NLCD), and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.AGB_Carbon_Sequestration_RGGI_1922
This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model. The ED Model integrates several key data including climate variables from Daymet and MERRA2 products; physical soil and hydraulic properties from Probabilistic Remapping of SSURGO (POLARIS) and CONUS-SOIL; land cover characteristics from airborne lidar, the National Agriculture Imagery Program (NAIP), and the National Land Cover Database (NLCD); and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.Maine_Forest_Biomass_Map_2435
This dataset holds estimates of forest aboveground biomass (AGB) for Maine, USA, in 2023. AGB was estimated using airborne LiDAR data from the USGS 3DEP project and a deep learning convolutional neural network (CNN) model. The airborne LiDAR datasets used in this mapping were collected in different years. The CNN model was calibrated using plot-level forest inventory data with precise location measurements and spectral indices derived from multiple remote sensing products. Stand-level biomass succession models, developed from the USDA Forest Service Forest Inventory and Analysis (FIA) data, were applied to project biomass estimates to the year 2023 with 10-m spatial resolution. The data are provided in GeoTIFF format.Annual_Forest_AGB_Maryland_2384
This dataset includes estimates of annual forest aboveground biomass over the state of Maryland, USA, for the period 1984-2023. It was generated by a modeling approach that linked an ecosystem model called Ecosystem Demography (ED) model, airborne lidar data of canopy height in circa 2010, and the remote sensing based land cover change dataset (NAFD).AGB_NEP_Disturbance_US_Forests_1829
This dataset, derived from the National Forest Carbon Monitoring System (NFCMS), provides estimates of forest carbon stocks and fluxes in the form of aboveground woody biomass (AGB), total live biomass, total ecosystem carbon, aboveground coarse woody debris (CWD), and net ecosystem productivity (NEP) as a function of the number of years since the most recent disturbance (i.e., stand age) for forests of the conterminous U.S. at a 30 m resolution for the benchmark years 1990, 2000, and 2010. The data were derived from an inventory-constrained version of the Carnegie-Ames-Stanford Approach (CASA) carbon cycle process model that accounts for disturbance processes for each combination of forest type, site productivity, and pre-disturbance biomass. Also provided are the core model data inputs including the year of the most recent disturbance according to the North American Forest Dynamics (NAFD) and the Monitoring Trends in Burn Severity (MTBS) data products; the type of disturbance; biomass estimates from the year 2000 according to the National Biomass and Carbon Dataset (NBCD); forest-type group; a site productivity classification; and the number of years since stand-replacing disturbance. The data are useful for a wide range of applications including monitoring and reporting recent dynamics of forest carbon across the conterminous U.S., assessment of recent trends with attribution to disturbance and regrowth drivers, conservation planning, and assessment of climate change mitigation opportunities within the forest sector.Forest_Inventory_Brazil_2007
This dataset provides the complete catalog of forest inventory and biophysical measurements collected over selected forest research sites across the Amazon rainforest in Brazil between 2009 and 2018 for the Sustainable Landscapes Brazil Project. This dataset includes measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories. Also included for each tree are the family, common and scientific names, coordinates, canopy position, crown radius, and for dead trees, the decomposition status. Aboveground biomass estimate is available for selected sites. The data are provided in comma-separated values (CSV) and shapefile formats. Sampling methodology for each site and year is described in companion files.GEOS_CASAGFED_3H_NEE
This product provides 3 hourly average net ecosystem exchange (NEE) and gross ecosystem exchange (GEE) of Carbon derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.GEOS_CASAGFED_D_FIRE
This product provides Daily average wildfire emissions (FIRE) and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.GEOS_CASAGFED_M_FLUX
This product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_Global_Fire_Atlas_1642
The Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product. The algorithm identified 13.3 million individual fires (>=21 ha or 0.21 km2; the size of one MODIS pixel) over the 2003-2016 study period.GlobFireCarbon
This dataset provides carbon monoxide and carbon dioxide flux from fires constrained by satellite observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_Global_Forest_AGC_2180
This dataset provides global gridded estimates of forest aboveground carbon stocks and potential fluxes at a 0.01-degree resolution. It was derived by initializing a newly developed global Ecosystem Demography model (ED v3.0) with novel remote sensing observations of tree canopy height collected by GEDI and ICESat-2, two NASA spaceborne lidar missions. A total of 3.77 billion lidar samples were used to generate gridded canopy height histograms that were then linked to ED simulations of canopy height and carbon dynamics during ecosystem succession. This process constrained representation of contemporary forest conditions and associated carbon stocks and fluxes in the model. Inputs that drove these simulations included meteorology, carbon dioxide levels, and soil properties. The data are provided in cloud-optimized GeoTIFF format.Methane_Flaring_Sites_VIIRS_1874
This dataset contains annual global flare site surveys from 2012-2019 derived from Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (SNPP) satellite. Gas flaring sites were identified from heat anomalies first estimated by the VIIRS Nightfire (VNF) algorithm from which high-temperature biomass burning and low-temperature gas flaring were separated based on temperature and persistence. Nightly observations for each flare site were drawn to determine their activity in the given calendar year. Data include flare location, temperature, and estimated flared gas volume; flaring data summarized by country; and KMZ files for viewing flaring locations in Google Earth. This dataset is valuable for measuring the current status of global gas flaring, which can have significant environmental impacts.CMS_Global_Soil_Respiration_1736
This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global-scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.SoilResp_HeterotrophicResp_1928
This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016.GFEI_CH4
This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries. The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.CMS_Global_Map_Mangrove_Canopy_1665
This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.CMS_Global_Mangrove_Loss_1768
This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.CMSGCH4F
This dataset provides global methane fluxes optimized with GOSAT data for 2010-2018. It is supported by the Carbon Monitoring System project. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.Global_Salt_Marsh_Change_2122
This dataset provides global salt marsh change, including loss and gain for five-year periods from 2000-2019. Loss and gain at a 30 m spatial resolution were estimated with Normalized Difference Vegetation Index (NDVI) anomaly algorithm using Landsat 5, 7, and 8 collections within the known extent of salt marshes. The data are provided in cloud-optimized GeoTIFF format.Global_Freshwater_CH4Emissions_2253
This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones. Weekly mean CH4 fluxes were computed from half-hourly FLUXNET-CH4 Version 1.0 fluxes. Each grid cell CH4 flux prediction was weighted by fractional grid cell wetland extent to estimate CH4 emissions using the primary global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) product and an alternate Global Inundation Estimate from Multiple Satellites GIEMS version 2 global wetland map. Both WAD2M and GIEMS-2 maps were modified with several correction data layers to represent the monthly area covered by vegetated wetlands, excluding open water and coastal wetlands. The data products are: mean daily fluxes with no adjustment for wetland area (i.e., flux densities assuming hypothetical 100% wetland cover); mean daily fluxes adjusting for WAD2M or GIEMS-2 wetland area; and by-pixel monthly sum of freshwater wetland methane emissions adjusting for WAD2M or GIEMS-2 wetland area. The data are provided in NetCDF4 format.Tidal_Marsh_Vegetation_US_1608
This dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water. Using this high-resolution map, the percent of each class within Landsat pixel extents was calculated to produce a 30m fraction of green vegetation map for each region.CStocks_Greenness_Mangroves_MX_1853
This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries. One-time estimates of aboveground carbon and soil organic carbon stocks for the PMFM derived from existing sources are provided. Also included are the monthly mean normalized difference vegetation index (NDVI) from MOD13A3 used to derive greenness trends, monthly mean air temperature, and total monthly precipitation from Daymet for 2001-2015 across the PMFM. Other files include the distribution and coverage of PMFM across Mexico. Distributions are provided as four categories of PMFM: (1) Arid mangroves with Surface Water as main input, along the Gulf of California and Pacific Coast (ARsw); (2) humid mangroves with surface water input along the Pacific Coast (HUsw-Pa); (3) humid mangroves with surface water input along the coast of the Gulf of Mexico (HUsw-Gf); (4) humid mangroves with groundwater input along the Gulf of Mexico and Caribbean Sea (HUgw). These data provide a baseline for national monitoring programs, carbon accounting models, and greenness trends in coastal wetlands.Tidal_Wetland_GPP_CONUS_1792
This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or "blue carbon," is a valued resource that is increasingly important for restoration and conservation purposes.SIF_PAR_fPAR_US_Midwest_2018_1813
This dataset provides estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF under clear-sky and real/cloudy conditions at a resolution of 4 km for the Midwest USA. The estimates are 8-day averaged daily means over the 2018 crop growing season for the time period 2018-05-01 to 2018-09-29. SIF of a specific vegetation type (i.e., corn, soybean, grass/pasture, forest) was expressed as the product of photosynthetically active radiation (PAR), the fraction of photosynthetically active radiation absorbed by the canopy (fPAR), and canopy SIF yield (SIFyield) for each vegetation type. Uncertainty of each variable was also calculated and is provided. These components of the SIF model were derived using a TROPOspheric Monitoring Instrument (TROPOMI) dataset, the USDA National Agricultural Statistics Service Cropland Data Layer, and the MODIS MCD15A2H 8-day 500 m fPAR product. These data could be used to improve estimates of vegetation productivity and vegetation stress.CMS_HR_MNA_CH4_FLUX
This data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_Landcover_Indonesia_1838
This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.Estimated_Biomass_Stock_Amazon_1648
This dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas. The PALSAR data across the three focal areas was combined and used in a GBM model to predict forest aboveground biomass across the entire Paragominas municipality.CMS_LiDAR_Biomass_MD_PA_DE_1538
This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation. Field-based estimates were then related to LiDAR height and volume metrics through random forest regression models across three physiographic regions. Spatial errors were estimated at the pixel level using standard prediction intervals to assess the accuracy of the modeling approach. Estimates of biomass were further validated against the permanent network of FIA plots and compared with existing coarse resolution national biomass maps.AGB_CanopyHt_Cover_NewEngland_1854
This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.Forest_AGB_NW_USA_1766
This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.LiDAR_Forest_Inventory_Brazil_1644
This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.CMS_LiDAR_AGB_California_1537
This dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized "remote sensing aware" allometric equation to these individual trees. Aboveground biomass estimates and their uncertainties for each study area are provided in per-tree and gridded format. The canopy height models used for the tree detection and biomass estimation are also provided.CMS_CH4_FLX_CA
This data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. A related data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. The Canadian emissions are concentrated in Alberta (gas production and processing) and the Mexican emissions are concentrated along the east coast (oil production). More details about the observations, algorithm, and scientific findings are described in Sheng et al. 2017. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_CH4_FLX_MX
This data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. A related data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. The Mexican emissions are concentrated along the east coast (oil production) and the Canadian emissions are concentrated in Alberta (gas production and processing). More details about the observations, algorithm, and scientific findings are described in Sheng et al. 2017. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_CH4_FLX_NAD
The CMS Methane (CH4) Flux for North America data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The nested approach of the inversion enables large point sources to be resolved while aggregating regions with weak emissions and minimizing aggregation errors. The emission sources are separated into 12 different sectors as follows: Total, Oil/Gas, Coal, Cows, Waste (Landfills+ Wastewater), Biofuel, Rice, Other Anthropogenic, Biomass Burning, Wetlands, Soil Absorption, Other Natural. More details about the algorithm and error characterization can be found in Turner, Jacob, Wecht, et al. 2015. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.Methane_Ethane_MA_NH_1982
This dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS. Background CH4 concentrations for the urban sites were determined using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model trajectories at the boundary of the study region based on measurements at three boundary sites and wind direction from the North American Mesoscale Forecast System (NAM) 12-kilometer meteorology.CH4_Plume_AVIRIS-NG_1727
This dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey. The survey was designed to cover at least 60% of the methane point source infrastructure in California guided by the Vista-CA dataset of identified locations of potential methane emitting facilities and infrastructure in three primary sectors (energy, agriculture, and waste). The purpose of the survey was to detect, quantify, and attribute point source emissions to specific infrastructure elements to improve the scientific understanding of regional methane budgets and to inform policy and planning activities that reduce methane emissions.MICASA_FLUX_3H
MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al. (1993), diverging in development since Randerson et al. (1996). CASA is a light use efficiency model: NPP is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of photosynthetically active radiation (fPAR) by the vegetation canopy derived from satellite data. Fire parameterization was incorporated into the model by van der Werf et al. (2004) leading to CASA-GFED3 after several revisions (van der Werf et al., 2006, 2010). Development of the GFED module has continued, now at GFED5 (Chen et al., 2023) with less focus on the CASA module. MiCASA diverges from GFED development at version 3, although future reconciliation is possible. Input datasets include air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. These products are primarily based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined datasets including land cover classification (MCD12Q1), burned area (MCD64A1), Nadir BRDF-Adjusted Reflectance (NBAR; MCD43A4), from which fPAR is derived, and tree/herbaceous/bare vegetated fractions from Terra only (MOD44B). Emissions due to fire and burning of coarse woody debris (fuel wood) are estimated separately.SLOPE_GPP_CONUS_1786
This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1.NASMo_TiAM_250m_2326
This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format.BGC_glider_GNATS
This dataset contains ocean biogeochemistry data from two Slocum gliders along the Gulf of Maine North Atlantic Time Series (GNATS) transect. The transect runs approximately east-west, with only a very minor change in latitude. The gliders are deployed on the western end of the transect, travel along the transect line to the eastern end, turn around and travel back along the transect to the western end, before being recovered. Each file contains data from one deployment (a glider “mission”), and thus contains both an eastbound and a westbound measurement of each variable. A full mission takes approximately 20 – 30 days. The data are gridded by longitude (0.01° intervals) and depth (1 m intervals). For more details on dataset preparation, see the Original Publication Citation and Data Processing Workflow below.CMS_OCE_BGC_CCS
A coupled physical-biogeochemical ocean model (the MITgcm with BLING biogeochemistry) is a least squares fit to all available ocean observations in the region of the California Current System. This is accomplished iteratively through the adjoint method, using the methodology developed by the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO). The result is a physically realistic estimate of the ocean state. The model domain extends from 28N to 40N and from 130W to 114W. It has a 1/16-degree horizontal resolution (~7km) and 72 vertical levels. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.CMS_Monthly_CO2_Gulf_1668
This dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of America for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations. Estimates of CO2 flux were derived from estimates of seawater pCO2, wind fields, and atmospheric pCO2.Coastal_US_Elevation_Data_1844
This dataset provides maps of the elevation of coastal wetlands relative to tidal ranges for the conterminous United States (CONUS) at 30 m resolution for 2010. It also includes maps of tidal amplitude, relative sea-level rise for the period 1983-2001, and maps for coastal lands and low marsh areas based on the probability of being below the mean higher high tide water line for spring tides (MHHWS). Uncertainty layers for elevation maps are also provided.CMS_Coastal_Wetland_Resilience_1839
This dataset provides information about the resilience of tidal wetlands to sea-level rise under three scenarios of global change. With rising seas, regularly inundated tidal wetlands may persist by vertical accretion of sediments (vertical resilience) and/or by migrating inland (lateral resilience), but local and regional conditions constrain these options. This dataset provides a vertical resilience index (VR) for coastal wetlands at 30 m resolution across the continental US predicted for 2100. The VR index was computed for current sea levels, local tidal dynamics, and coastal topography. It was also calculated for future sea levels predicted for 2100 by three IPCC Realized Concentration Pathway (RCP) scenarios: 2.5, 4.5, and 8.5. Moreover, the VR index incorporates estimated rates of sediment accretion. Relevant to lateral resiliency, the data include current and future tidal areas identified by mapping mean higher high water spring tide locations under the RCP scenarios. A shapefile outlining watershed units with tidal wetlands is included along with land cover classes for these areas for 1996 and 2011.SatelliteDerived_Forest_Mexico_2320
This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.SiB4_Global_HalfDegree_Daily_1849
This dataset provides global daily output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Daily output includes carbon, carbonyl sulfide, and energy fluxes; solar-induced fluorescence; carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the "npft" dimension (01-15) in each data file. The PFT three-character abbreviations ("pft_names" variable) are listed in the same order as the "npft" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the "pft_area" variable for each cell.SiB4_Global_HalfDegree_Hourly_1847
This dataset provides global hourly output predicted from the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Hourly output includes carbon fluxes, carbonyl sulfide (COS) fluxes, gross primary production, ecosystem respiration, solar-induced fluorescence (SIF), top-layer soil temperature and moisture, vegetation stress, photosynthetically active radiation (PAR), leaf and canopy-level carbon-dioxide partial pressures, and canopy conductance. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the "npft" dimension (01-15) in each data file. The PFT three-character abbreviations ("pft_names" variable) are listed in the same order as the "npft" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the "pft_area" variable for each cell.SiB4_Global_HalfDegree_Monthly_1848
This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Monthly output includes carbon, carbonyl sulfide (COS), and energy fluxes; solar-induced fluorescence (SIF); carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the "npft" dimension (01-15) in each data file. The PFT three-character abbreviations ("pft_names" variable) are listed in the same order as the "npft" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the "pft_area" variable for each cell.Crops_SIF_VegIndices_IL_NE_2136
This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM_nonlinear and SFM_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f_cal_corr_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.Wetland_Soil_CarbonStocks_WA_2249
This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023). Wetland intrinsic potential estimates the likelihood that that an area is a wetland using a random forest model built on vegetation, hydrology, and soil data (Halabisky et al., 2022). SOC estimates at 1 m and 30 cm, SOC standard deviations, and WIP are presented in Cloud-Optimized GeoTIFF (.tif) format at 4-m resolution. Also included are 36 field observations of SOC collected from 2020-08-01 to 2022-06-29. These are contained in a comma separated (.csv) file.CMS_SOC_Mexico_1754
This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI). The profile data were used for the development of a predictive model along with a set of environmental covariates that were harmonized in a regular grid of 90x90 m2 across all Mexican states. The base of reference was the digital elevation model (DEM) of the INEGI at 90-m spatial resolution. A model ensemble of regression trees with a recursive elimination of variables explained 54% of the total variability using a cross-validation technique of independent samples. The error associated with the predictive model estimates of SOC is provided. A summary of the total estimated SOC per state, statistical description of the modeled SOC data, and the number of pixels modeled for each state are also provided.CMS_SOC_Mexico_CONUS_1737
This dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction. Predictions of gridded SOC and uncertainty based on multiple bulk density (BD) pedotransfer functions (PFTs) are also included. The second set of gridded SOC estimates, for the period 2009-2011, were derived from two fully independent validation field datasets from across both countries. Note that the same environmental variables and modeling approach used for the first set of estimates were applied to the second set to assess the models' sensitivity to multiple SOC data sources. The SOC field data for the first set of estimates are provided in this dataset and the other data sources, including the two independent validation field datasets, are referenced.Country_SOC_Latin_America_1615
This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.Tidal_Wetland_Soil_Carbon_1612
This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data. All 30 x 30-meter C-CAP pixels were extracted that are coded as estuarine emergent, scrub/shrub, or forested in either 2006 or 2010. A soil database for model fitting and validation was compiled from 49 different studies with spatially explicit empirical depth profile data and associated metadata, totaling 1,959 soil cores from 18 of the 22 coastal states. Reported estimates of carbon stocks were derived with modeling approaches that included (1) applying a single average carbon stock value from the compiled soil core data, (2) applying models fit using the empirical data and applied spatially using soil, vegetation and salinity maps, (3) relying on independently generated soil carbon maps from The United States Department of Agriculture (USDA)'s Soil Survey Geographic Database (SSURGO), and the NWI that intersected with mapped tidal wetlands, and (4) using a version of SSURGO bias-corrected for bulk density. Comparisons of uncertainty, precision, and accuracy among these four approaches are also provided.Tree_Canopy_Cover_Mexico_2137
The data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022). The hierarchical U-Net framework first developed a U-Net model for very high-resolution aerial images (NAIP) using training labels derived from previous work based on an interactive image segmentation tool and iterative updates with expert knowledge (Basu et al., 2015). The developed NAIP U-Net model and NAIP data produced 1-m NAIP TC across all lower 48 CONUS states. A Landsat U-Net model was developed for multi-year and large-scale TC mapping based on the very high-resolution NAIP TC made in the earlier stage. The Landsat U-Net model developed was adopted over the CONUS for testing its transferability, validation, and improvement across Mexico. This dataset provides national-scale percent tree cover estimates over Mexico and can be helpful for studies of carbon cycling, land cover and land use change, etc. The team has been working on improving temporal stability of the product and will update the product once the next version is ready to be shared.High_Res_Tidal_Marsh_Veg_1609
This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map. These 1m resolution maps were used to calculate the fraction of green vegetation within 30m Landsat pixels for the same tidal marsh regions and these data are provided in a related dataset.Wetland_Salinity_Maps_2392
This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis. Data were derived from a hybrid approach to mapping salinity as a continuous variable using a combination of physical watershed and stream characteristics, optical remote sensing based on vegetation characteristics, and climate variables. Data are provided in cloud-optimized GeoTIFF format covering 33 Hydrologic Unit Code 8-digit (HUC8) watersheds to the extent of palustrine and estuarine wetlands as defined by NOAA's 2016 Coastal Change Analysis Program (C-CAP) Coastal Land Cover layer. Additionally, model outputs are provided in comma separated values (CSV) files, and code scripts are provided in a compressed (.zip) file.Data Discovery
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Resources on AWS
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- AGB_Pantropics_Amazon_Mexico_1824 v1 - This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes.
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arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_Pantropics_Amazon_Mexico/data- AWS Region
us-west-2
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- Salt_Marsh_Biomass_CONUS_2348 v1 - This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression.
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- S3 Bucket Controlled Access
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arn:aws:s3:::ornl-cumulus-prod-protected/cms/Salt_Marsh_Biomass_CONUS/data- AWS Region
us-west-2
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- Description
- Howland_Forest_Biomass_Map_2434 v1 - This dataset holds aboveground biomass (AGB) estimates at 10-m spatial resolution for the Howland Research Forest in central Maine for 2012, 2015, 2017, 2021, and 2023. Forest inventory data were collected using 50 fixed-area plot sampling during the summers of 2021, 2023, and 2024.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Howland_Forest_Biomass_Map/data- AWS Region
us-west-2
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- Description
- Tidal_Marsh_Biomass_US_V1-1_1879 v1.1 - This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Marsh_Biomass_US_V1-1/data- AWS Region
us-west-2
-
- Description
- CMS_AGB_Landcover_Indonesia_1645 v1 - This dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_AGB_Landcover_Indonesia/data- AWS Region
us-west-2
-
- Description
- CMS_iWED_V1_2452 v1 - This dataset provides an integrated Wildfire Event Dataset (iWED version 1) for wildfire events of 100 ha or more in area from 1992 to 2021 for the continental US. Fire information was compiled from a variety of state, regional, and federal-level agencies responsible for filing and archiving incident level reports.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_iWED_V1/data- AWS Region
us-west-2
-
- Description
- CMS_AGB_NW_USA_1719 v1 - This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_AGB_NW_USA/data- AWS Region
us-west-2
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- Description
- Annual_Burned_Area_Maps_1708 v1 - This dataset provides maps of annual burned area for the part of Mawas conservation program in Central Kalimantan, Indonesia from 1997 through 2015. Landsat imagery (TM, ETM+, OLI/TIR) at 30 m resolution was obtained for this 19-year period, including the variables surface reflectance, brightness temperature, and pixel quality assurance, plus the indices NDVI, NDMI, NBR, NBR2, SAVI, and MSAVI.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Annual_Burned_Area_Maps/data- AWS Region
us-west-2
-
- Description
- CMS_WFEIS_CONUS-AK_1306 v1 - This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_WFEIS_CONUS-AK/data- AWS Region
us-west-2
-
- Description
- Blue_Carbon_Tidal_Wetland_Maps_2091 v1 - This dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Blue_Carbon_Tidal_Wetland_Maps/data- AWS Region
us-west-2
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- Description
- BlueFlux_AirborneObs_Florida_2327 v1 - This dataset includes airborne in situ measurements of greenhouse gas mixing ratios, meteorological parameters, and fluxes (CO2, CH4, latent heat fluxes, friction velocity, and convective velocity scale) calculated with wavelet transforms. CO2, CH4, CO, O3, and water vapor mixing ratios, and meteorological variables were obtained from a Beechcraft A90 King Air aircraft.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/BlueFlux_AirborneObs_Florida/data- AWS Region
us-west-2
-
- Description
- BlueFlux_Tidal_River_Water_2333 v1 - This dataset provides dissolved carbon (dissolved inorganic carbon and dissolved organic carbon), greenhouse gases, dissolved organic matter optical, and hydrological (water temperature, pH, alkalinity, dissolved oxygen) data collected from the Shark and Harney tidal rivers in the Everglades, Florida, USA. The data were collected as part of the NASA Carbon Monitoring System (CMS) BlueFlux field campaigns over the 2022 wet season (October 2022) and 2023 dry season (March 2023).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/BlueFlux_Tidal_River_Water/data- AWS Region
us-west-2
-
- Description
- BlueFlux_Gridded_CO2_CH4_2404 v1 - This dataset contains gridded estimates of carbon dioxide (CO2) and methane (CH4) fluxes at daily resolution covering the Southern Florida region from 2000 to 2024. Gridded CO2 and CH4 flux prototype products at 500-m spatial resolution were derived from a machine learning model based on eddy covariance (EC) measurements from 1) airborne fluxes collected seasonally with the NASA Carbon Airborne Flux Experiment (CARAFE) over the region during five flight deployments and 2) regional EC tower networks representing long term wetland ecosystem fluxes since 2004.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/BlueFlux_Gridded_CO2_CH4/data- AWS Region
us-west-2
-
- Description
- TLS_Lidar_BlueFlux_Mangroves_2311 v1 - This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS "Blueflux" campaigns in March 2022, October 2022, and March 2023.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/TLS_Lidar_BlueFlux_Mangroves/data- AWS Region
us-west-2
-
- Description
- Boreal_Arctic_Wetland_CH4_2351 v1 - This dataset provides an upscaled estimate of Boreal-Arctic wetland CH4 emissions at a weekly time scale from 2002 to 2021 at 0.5 by 0.5-degree spatial resolution. Ground truth data on wetland CH4 emissions from eddy covariance towers (139 site years) and chambers (168 site years) were used to train and validate a causality-guided machine learning model.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Boreal_Arctic_Wetland_CH4/data- AWS Region
us-west-2
-
- Description
- CMSFluxFire v1 - This dataset provides the Carbon Flux for Fires. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFire.1- AWS Region
us-west-2
-
- Description
- CMSFluxFossilFuelPrior v2 - This dataset provides the Carbon Flux for Fossil Fuel Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFossilFuelPrior.2- AWS Region
us-west-2
-
- Description
- CMSFluxOceanPrior v2 - This dataset provides the Carbon Flux for Ocean Carbon Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOceanPrior.2- AWS Region
us-west-2
-
- Description
- CMSFluxFossilFuelPrior v3 - This dataset provides the Prior for the Fossil Fuel Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFossilFuelPrior.3- AWS Region
us-west-2
-
- Description
- CMSFluxNBE v2 - This dataset provides the Carbon Flux from the Net Biome Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNBE.2- AWS Region
us-west-2
-
- Description
- CMSFluxNBEPrior v2 - This dataset provides the Carbon Flux from the Net Biome Exchange Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNBEPrior.2- AWS Region
us-west-2
-
- Description
- CMSFluxLandPrior v3 - This dataset provides the Prior for the Land Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxLandPrior.3- AWS Region
us-west-2
-
- Description
- CMSFluxNBE v3 - This dataset provides the Carbon Flux for Posterior Net Biome Exchange (NBE). The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNBE.3- AWS Region
us-west-2
-
- Description
- CMSFluxOcean v3 - This dataset provides the Posterior Carbon Flux for the Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOcean.3- AWS Region
us-west-2
-
- Description
- CMSFluxOceanPrior v3 - This dataset provides the Prior for the Carbon Flux for Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOceanPrior.3- AWS Region
us-west-2
-
- Description
- CMSFluxTotal v2 - This dataset provides the Carbon Flux for Posterior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxTotal.2- AWS Region
us-west-2
-
- Description
- CMSFluxTotalPrior v3 - This dataset provides the Prior for Total Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxTotalPrior.3- AWS Region
us-west-2
-
- Description
- CMSFluxFossilfuel v1 - This dataset provides the Carbon Flux for Fossil Fuel. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFossilfuel.1- AWS Region
us-west-2
-
- Description
- CMSFluxOcean v1 - This dataset provides the Carbon Flux for Ocean Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOcean.1- AWS Region
us-west-2
-
- Description
- CMSFluxTotalprior v1 - This dataset provides the Carbon Flux for Prior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxTotalprior.1- AWS Region
us-west-2
-
- Description
- CMSFluxMISC v1 - This dataset provides the Carbon Flux for Shipping, Aviation, and Chemical Sources. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxMISC.1- AWS Region
us-west-2
-
- Description
- CMSFluxNEE v1 - This dataset provides the Carbon Flux from the Net Ecosystem Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNEE.1- AWS Region
us-west-2
-
- Description
- CMSLakeHuronPPM v1 - Monthly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeHuronPPM.1- AWS Region
us-west-2
-
- Description
- CMSLakeHuronPPY v1 - Yearly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeHuronPPY.1- AWS Region
us-west-2
-
- Description
- CMSLakeMichiganPPM v1 - Monthly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeMichiganPPM.1- AWS Region
us-west-2
-
- Description
- CMSLakeMichiganPPY v1 - Yearly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeMichiganPPY.1- AWS Region
us-west-2
-
- Description
- CMSLakeSuperiorPPM v1 - Monthly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeSuperiorPPM.1- AWS Region
us-west-2
-
- Description
- CMSLakeSuperiorPPY v1 - Yearly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeSuperiorPPY.1- AWS Region
us-west-2
-
- Description
- CMS_CONUS_Biomass_1752 v1 - This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_CONUS_Biomass/data- AWS Region
us-west-2
-
- Description
- CMS_CTL_NA_GOSAT_FOOTPRINTS v1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the GOSAT satellite. Meteorological fields from the WRF model are used to drive STILT.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CTL_NA_GOSAT_FOOTPRINTS.1- AWS Region
us-west-2
-
- Description
- CMS_CTL_NA_OCO2_FOOTPRINTS v1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CTL_NA_OCO2_FOOTPRINTS.1- AWS Region
us-west-2
-
- Description
- CMS_CTL_NA_TCCON_FOOTPRINTS v1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the TCCON ground network. Meteorological fields from the WRF model are used to drive STILT.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CTL_NA_TCCON_FOOTPRINTS.1- AWS Region
us-west-2
-
- Description
- CMS_Global_Forest_Age_2345 v1 - This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Forest_Age/data- AWS Region
us-west-2
-
- Description
- CMS_Simulated_SIF_NiwotRidge_1720 v1 - This dataset provides results for simulations of solar-induced chlorophyll fluorescence (SIF) implemented within the terrestrial biosphere Community Land Model (CLM 4.5) for Niwot Ridge, Colorado, USA, from 1998-2018. The data include outputs from three model simulations designed to test the importance of non-photochemical quenching (NPQ), that is, the absorbed light energy dissipated as heat, in determining seasonal SIF.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Simulated_SIF_NiwotRidge/data- AWS Region
us-west-2
-
- Description
- C_FluxStocks_CLM5_DART_WestUS_1856 v1 - This dataset provides monthly estimates of biomass stocks and land-atmosphere carbon exchange across the western United States at 0.95 degrees longitude x 1.25 degrees latitude grid resolution from 1998 through 2010. The data include outputs from two types of model simulations: (1) a "free" simulation which used Community Land Model (CLM5.0) simulations forced with meteorology appropriate for complex mountainous terrain, and (2) "assimilation" runs using the land surface data assimilation system (CLM5-DART).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/C_FluxStocks_CLM5_DART_WestUS/data- AWS Region
us-west-2
-
- Description
- CMS_GO_CH4_SEC_TDYC_NA v1 - Methane emissions are provided by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), En- vironment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as...
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_GO_CH4_SEC_TDYC_NA.1- AWS Region
us-west-2
-
- Description
- CMS_FluxEstimates_Aircraft_CO2_2336 v1 - This dataset provides gridded surface-atmosphere CO2 fluxes over North America from April 8 to November 18 during 2018 and 2019. Net ecosystem exchange (NEE) was estimated by the CMS-Flux-NA CO2 inversion system by assimilating in situ CO2 measurements and/or Orbiting Carbon Observatory (OCO-2) column-averaged CO2 retrievals.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_FluxEstimates_Aircraft_CO2/data- AWS Region
us-west-2
-
- Description
- CMS_Mangrove_Biomass_Zambezi_1522 v1 - This dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Biomass_Zambezi/data- AWS Region
us-west-2
-
- Description
- CMS_LiDAR_AGB_PEF_2012_1318 v1 - This data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_AGB_PEF_2012/data- AWS Region
us-west-2
-
- Description
- Global_Riverine_N2O_Emissions_1791 v1 - This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Global_Riverine_N2O_Emissions/data- AWS Region
us-west-2
-
- Description
- Atmospheric_CO2_California_1641 v1 - This dataset provides measurements of atmospheric CO2 concentrations, carbon isotopes d13C and D14C, and fossil fuel CO2 (ffCO2) estimates from nine observation sites in California over three month-long campaigns in separate seasons of 2014-2015. ffCO2 was quantified based on the CO2 concentration and D14C.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Atmospheric_CO2_California/data- AWS Region
us-west-2
-
- Description
- CMS_Methane_Emissions_Boston_1291 v1 - This data set provides average hourly measured, modeled enhancements, and background methane (CH4) concentrations, atmospheric ethane (C2H6) measurements, prior CH4 flux fields by sector, and a spatial reconstruction of natural gas (NG) consumption in Boston, Massachusetts and the surrounding region. Atmospheric CH4 concentrations were measured continuously from September 2012 through August 2013 at four locations and atmospheric ethane was measured continuously for several months during 2012-2014 at one location.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Methane_Emissions_Boston/data- AWS Region
us-west-2
-
- Description
- CMS_Global_Cropland_Carbon_1279 v1 - This data set provides global estimates of carbon fluxes associate with annual crop net primary production (NPP) and harvested biomass, annual uptake and release by humans and livestock, and the total annual estimate of net carbon exchange (NCE) derived from these carbon fluxes. NCE estimates are for the combined crop plant harvest and consumption/expiration of fodder by livestock and of food by humans.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Cropland_Carbon/data- AWS Region
us-west-2
-
- Description
- CMS_WRF_Footprints_CO2_Signals_1381 v1 - This data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_WRF_Footprints_CO2_Signals/data- AWS Region
us-west-2
-
- Description
- GPP_CONUS_TROPOMI_1875 v1 - This dataset includes estimates of gross primary production (GPP) for the conterminous U.S., for 2018-02-15 to 2021-10-15, based on measurements of solar-induced chlorophyll fluorescence from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite platform. GPP was estimated from rates of photosynthesis inferred from SIF using a linear model and ecosystem scaling factors from 102 AmeriFlux sites.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/GPP_CONUS_TROPOMI/data- AWS Region
us-west-2
-
- Description
- CMS_Pantropical_Forest_Biomass_1337 v1 - This data set provides estimates of pre-deforestation aboveground live woody biomass (AGLB) at 30-m resolution for deforested areas of tropical America, tropical Africa, and tropical Asia for the year 2000. The biomass estimates are only for areas where deforestation occurred during the period 2000 through 2012.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Pantropical_Forest_Biomass/data- AWS Region
us-west-2
-
- Description
- CMS_Daily_ET_MexFlux_1309 v1 - This data set provides daily average observations for evapotranspiration (measured and gap-filled), precipitation, net radiation, soil water content, air temperature, vapor pressure deficit, and normalized vegetation index (NDVI) from two water-limited shrubland sites for years 2008-2010. Both sites are located in the northwest part of Mexico and are part of the MexFlux network.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Daily_ET_MexFlux/data- AWS Region
us-west-2
-
- Description
- CMS_Fire_Weather_Data_AK_1509 v1 - This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Fire_Weather_Data_AK/data- AWS Region
us-west-2
-
- Description
- FIA_Forest_Biomass_Estimates_1873 v1 - This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/FIA_Forest_Biomass_Estimates/data- AWS Region
us-west-2
-
- Description
- CMS_Forest_Productivity_1221 v1 - Notice: This data set and guide were updated on June 30, 2014 to correct an error in the reported units. The data values were not changed.Spatially-gridded estimates of above ground biomass (AGB), net primary productivity (NPP), and net ecosystem productivity (NEP) are provided for forested areas of the conterminous United States (CONUS).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Forest_Productivity/data- AWS Region
us-west-2
-
- Description
- CMS_Forest_Carbon_Fluxes_1313 v1 - This data set provides maps of estimated carbon in forests of the 48 continental states of the US for the years 2005-2010. Carbon (termed committed carbon) stocks were estimated for forest aboveground biomass, belowground biomass, standing dead stems, and litter for the year 2005.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Forest_Carbon_Fluxes/data- AWS Region
us-west-2
-
- Description
- CMS_Landscapes_Brazil_Forests_1301 v1 - This data set provides measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories taken at the Fazenda Cauaxi and the Fazenda Nova Neonita, Paragominas municipality, Para, Brazil. Also included for each tree are the common, family, and scientific name, coordinates, canopy position, crown radius, and for dead trees the decomposition status.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Landscapes_Brazil_Forests/data- AWS Region
us-west-2
-
- Description
- LIDAR_FOREST_CANOPY_HEIGHTS_1271 v1 - This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/LIDAR_FOREST_CANOPY_HEIGHTS/data- AWS Region
us-west-2
-
- Description
- CMS_Global_Monthly_Wetland_CH4_1502 v1 - This data set provides global monthly wetland methane (CH4) emissions and uncertainty data products derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies. The data are at 0.5 by 0.5-degree resolution.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Monthly_Wetland_CH4/data- AWS Region
us-west-2
-
- Description
- MonthlyWetland_CH4_WetCHARTsV2_2346 v1.3.3 - This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/MonthlyWetland_CH4_WetCHARTsV2/data- AWS Region
us-west-2
-
- Description
- CMS_Global_Livestock_CH4_CO2_1329 v2 - This data set provides global annual carbon flux estimates, at 0.05-degree resolution, associated with livestock feed intake, manure, manure management, respiration, and enteric fermentation, summed over all livestock types. These fluxes can be summed across multiple grid cells to obtain totals for any given areas.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Livestock_CH4_CO2/data- AWS Region
us-west-2
-
- Description
- CMS_Global_Mangrove_Forest_Ht_2251 v1 - This dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Mangrove_Forest_Ht/data- AWS Region
us-west-2
-
- Description
- CMS_WRF_Model_Products_1338 v1 - This data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_WRF_Model_Products/data- AWS Region
us-west-2
-
- Description
- GCAM_Land_Cover_2005-2095_1216 v1 - The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/GCAM_Land_Cover_2005-2095/data- AWS Region
us-west-2
-
- Description
- Landcover_Colombian_Amazon_1783 v1 - This dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Landcover_Colombian_Amazon/data- AWS Region
us-west-2
-
- Description
- Sonoma_County_Forest_AGB_1764 v1 - This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Sonoma_County_Forest_AGB/data- AWS Region
us-west-2
-
- Description
- CMS_Landscapes_Brazil_LiDAR_1302 v1 - This data set provides raw LiDAR point cloud data and derived Digital Terrain Models (DTMs) for five forested areas in the municipality of Paragominas, Para, Brazil, for the years 2012, 2013, and 2014. Data are included for two areas in Paragominas for 2013 and 2014, two areas for the Fazenda Cauaxi for 2012 and 2014, and for the Fazenda Andiroba for 2014.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Landscapes_Brazil_LiDAR/data- AWS Region
us-west-2
-
- Description
- CMS_LiDAR_Indonesia_1518 v1 - This dataset provides airborne LiDAR data collected over 90 sites totaling approximately 100,000 hectares of forested land in Kalimantan, Indonesia on the island of Borneo in late 2014. The data were collected as part of an effort to establish a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Indonesia/data- AWS Region
us-west-2
-
- Description
- CMS_LiDAR_Point_Cloud_Zambezi_1521 v1 - This data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Point_Cloud_Zambezi/data- AWS Region
us-west-2
-
- Description
- CMS_Maryland_AGB_Canopy_1320 v1 - This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Maryland_AGB_Canopy/data- AWS Region
us-west-2
-
- Description
- CMS_LiDAR_Biomass_CanHt_Sonoma_1523 v1 - This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Biomass_CanHt_Sonoma/data- AWS Region
us-west-2
-
- Description
- CMS_LiDAR_Products_Indonesia_1540 v1 - This dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Products_Indonesia/data- AWS Region
us-west-2
-
- Description
- CMS_Pilot_Biomass_1257 v1 - These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Pilot_Biomass/data- AWS Region
us-west-2
-
- Description
- CMS_Pennsylvania_Tree_Cover_1334 v1.1 - This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Pennsylvania_Tree_Cover/data- AWS Region
us-west-2
-
- Description
- CMS_Mangrove_CanHt_Stand_Age_1377 v1 - This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_CanHt_Stand_Age/data- AWS Region
us-west-2
-
- Description
- CMS_Mangrove_Canopy_Ht_Zambezi_1357 v1 - This data set provides high resolution canopy height estimates for mangrove forests in the Zambezi Delta, Mozambique, Africa. The estimates were derived from three separate canopy height models (CHM) using airborne Lidar data, stereophotogrammetry with WorldView 1 imagery, and Interferometric-Synthetic Aperture Radar (In-SAR) techniques with TanDEM-X imagery.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Canopy_Ht_Zambezi/data- AWS Region
us-west-2
-
- Description
- CMS_Mangrove_Canopy_Height_1327 v1 - This data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Canopy_Height/data- AWS Region
us-west-2
-
- Description
- CMS_Mangrove_Cover_1670 v1.1 - This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Cover/data- AWS Region
us-west-2
-
- Description
- CMS_CO2_Fluxes_TBMO_1315 v1 - This data set provides global, gridded, model-derived net ecosystem exchange (NEE) of CO2 flux between the land and atmosphere at 3-hourly time steps over seven years (2004-2010) at three different spatial resolutions: 0.5 x 0.5 degree, 2.0 x 2.5 degrees, and 4.0 x 5.0 degrees (latitude/longitude). The 3-hourly data were derived from monthly NEE outputs of 15 global land surface models and four ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_CO2_Fluxes_TBMO/data- AWS Region
us-west-2
-
- Description
- CMS_SST_GPP_Mexico_1310 v1 - This data set provides data for MODIS-derived (1) gross primary productivity (GPP) for the years 2000-2010, (2) fraction of photosynthetically active radiation (fPAR) for the years 2003-2013, (3) sea surface temperature (SST) for the years 2003-2013, and (4) the NOAA-source Multivariate ENSO Index (MEI) data for the years 2003-2013 (as a measure of the El Nino/Southern Oscillation). The study areas were three transects on the Baja California Peninsula, Mexico, and the adjacent Pacific Ocean.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SST_GPP_Mexico/data- AWS Region
us-west-2
-
- Description
- CMS_Great_Basin_Biomass_1755 v1 - This dataset provides annual maps of live aboveground tree biomass (Mg/ha) for pinyon-juniper forests across the Great Basin of the Western USA for the years 2000-2016 at a spatial resolution of 30 meters. Biomass estimates are limited to areas of the Great Basin defined as a pinyon-juniper ecosystem type by the 2016 Landfire Existing Vegetation Type map.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Great_Basin_Biomass/data- AWS Region
us-west-2
-
- Description
- CMS_SABGOM_Model_Simulations_1510 v1 - This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of America simulated by the South Atlantic Bight and Gulf of America (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SABGOM_Model_Simulations/data- AWS Region
us-west-2
-
- Description
- CMS_Soil_CO2_Efflux_1298 v1 - This data set provides the results of (1) monthly measurements of soil CO2 efflux, volumetric water content, and temperature, and (2) seasonal measurements of soil (porosity, bulk density, nitrogen (N) and carbon (C) content) and vegetation (leaf area index (LAI), litter and fine root biomass) properties in a water-limited ecosystem in Baja California, Mexico. Measurements and samples were collected from August 2011 to August 2012.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Soil_CO2_Efflux/data- AWS Region
us-west-2
-
- Description
- C_Pools_Fluxes_CONUS_1837 v1 - This dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/C_Pools_Fluxes_CONUS/data- AWS Region
us-west-2
-
- Description
- Vermont_HighRes_LandCover_2072 v1 - This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Vermont_HighRes_LandCover/data- AWS Region
us-west-2
-
- Description
- Tree_Canopy_Cover_Mexico_V2_2445 v2 - This dataset provides 20-year tree cover (TC) estimates at 30-m spatial resolution for Mexico from 2000 to 2019 using Landsat time series, airborne LiDAR, and machine learning. The TC data (hereafter, CMS-TC) offers accurate and consistent national-scale percent tree cover estimates with an overall coefficient of determination (R squared) of 0.81 and a root mean square error (RMSE) of 11.90%.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tree_Canopy_Cover_Mexico_V2/data- AWS Region
us-west-2
-
- Description
- Continuous_Lifeform_Maps_CONUS_1809 v1 - This dataset contains estimates of percent cover of tree, shrub, herb, and other (non-vegetation) lifeform classes and uncertainties for the conterminous U.S. (CONUS).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Continuous_Lifeform_Maps_CONUS/data- AWS Region
us-west-2
-
- Description
- Uncertainty_US_Coastal_GHG_1650 v1 - This dataset provides maps of coastal wetland carbon and methane fluxes and coastal wetland surface elevation from 2006 to 2011 at 30 m resolution for coastal wetlands of the conterminous United States. Total coastal wetland carbon flux per year per pixel was calculated by combining maps of wetland type and change with soil, biomass, and methane flux data from a literature review.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Uncertainty_US_Coastal_GHG/data- AWS Region
us-west-2
-
- Description
- Niwot_Ridge_CNPAM_Fluorescence_1722 v1 - This dataset provides chlorophyll fluorescence measurements made on pine and spruce needle tissues at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Two types of measurements were made using pulse-amplitude-modulation (PAM) fluorometry: the photosystem II (PSII) operating efficiency in the light (Fq''/Fm'' at variable light levels), and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) on dark-acclimated tissues.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Niwot_Ridge_CNPAM_Fluorescence/data- AWS Region
us-west-2
-
- Description
- Niwot_Ridge_Pigment_1723 v1 - This dataset provides concentrations of pigments in pine and spruce needle tissues collected at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Pigments measured included Chlorophyll A and B, Violaxanthin, Antheraxanthin, Zeaxanthin, Neoxanthin, Lutein, and beta-Carotene.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Niwot_Ridge_Pigment/data- AWS Region
us-west-2
-
- Description
- CMS_DARTE_V2_1735 v2 - This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_DARTE_V2/data- AWS Region
us-west-2
-
- Description
- GCRW_DEM_2016_1793 v1 - This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/GCRW_DEM_2016/data- AWS Region
us-west-2
-
- Description
- Disturbance_Biomass_Maps_1679 v1 - This dataset provides derived disturbance history and predicted annual forest biomass maps at 30-m resolution for six selected Landsat scenes across the Conterminous United States (CONUS) for the period 1985-2014. The focus sites are in the following states: Colorado, Maine, Minnesota, Oregon, Pennsylvania, and South Carolina.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Disturbance_Biomass_Maps/data- AWS Region
us-west-2
-
- Description
- Cropland_Carbon_Fluxes_2125 v1 - This dataset contains daily estimates of carbon fluxes in croplands derived from the "ecosys" model covering a portion of the Midwestern US (Illinois, Indiana, and Iowa) at county-level resolution from 2001-2018. Ecosys simulates water, energy, carbon, and nutrient cycles simultaneously for various ecosystems, including agricultural systems at up to hourly resolution.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Cropland_Carbon_Fluxes/data- AWS Region
us-west-2
-
- Description
- EF_Data_Mexico_1693 v1 - This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/EF_Data_Mexico/data- AWS Region
us-west-2
-
- Description
- CMS_EFT_CONUS_1659 v1 - This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_EFT_CONUS/data- AWS Region
us-west-2
-
- Description
- DLEM_C_N_Export_1699 v1 - This dataset provides estimates for export and leaching of dissolved inorganic carbon (DIC), dissolved organic carbon (DIC), total organic carbon (TOC), particulate organic carbon (POC), ammonium (NH4+), nitrate (NO3-), and total organic nitrogen (TON) from the Mississippi River Basin (MRB) to the Gulf of Mexico. The estimates are provided for a historical period of 1901-2014, and a future period of 2010-2099 (carbon estimates only) under two scenarios of high and low levels of population growth, economy, and energy consumption, respectively.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/DLEM_C_N_Export/data- AWS Region
us-west-2
-
- Description
- Fire_Emissions_Indonesia_2118 v1 - This dataset provides 10-minute fire emissions within 0.1-degree regularly spaced intervals across Indonesia from July 2015 to December 2020. The dataset was produced with a top-down approach based on fire radiative energy (FRE) and smoke aerosol emission coefficients (Ce) derived from multiple new-generation satellite observations.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Fire_Emissions_Indonesia/data- AWS Region
us-west-2
-
- Description
- CMS_Forest_Carbon_Maryland_1660 v1 - This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the Nation...
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Forest_Carbon_Maryland/data- AWS Region
us-west-2
-
- Description
- AGB_Carbon_Sequestration_RGGI_1922 v1 - This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_Carbon_Sequestration_RGGI/data- AWS Region
us-west-2
-
- Description
- Maine_Forest_Biomass_Map_2435 v1 - This dataset holds estimates of forest aboveground biomass (AGB) for Maine, USA, in 2023. AGB was estimated using airborne LiDAR data from the USGS 3DEP project and a deep learning convolutional neural network (CNN) model.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Maine_Forest_Biomass_Map/data- AWS Region
us-west-2
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- Description
- Annual_Forest_AGB_Maryland_2384 v1 - This dataset includes estimates of annual forest aboveground biomass over the state of Maryland, USA, for the period 1984-2023. It was generated by a modeling approach that linked an ecosystem model called Ecosystem Demography (ED) model, airborne lidar data of canopy height in circa 2010, and the remote sensing based land cover change dataset (NAFD).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Annual_Forest_AGB_Maryland/data- AWS Region
us-west-2
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- Description
- AGB_NEP_Disturbance_US_Forests_1829 v2 - This dataset, derived from the National Forest Carbon Monitoring System (NFCMS), provides estimates of forest carbon stocks and fluxes in the form of aboveground woody biomass (AGB), total live biomass, total ecosystem carbon, aboveground coarse woody debris (CWD), and net ecosystem productivity (NEP) as a function of the number of years since the most recent disturbance (i.e., stand age) for forests of the conterminous U.S. at a 30 m resolution for the benchmark years 1990, 2000, and 2010.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_NEP_Disturbance_US_Forests/data- AWS Region
us-west-2
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- Description
- Forest_Inventory_Brazil_2007 v1 - This dataset provides the complete catalog of forest inventory and biophysical measurements collected over selected forest research sites across the Amazon rainforest in Brazil between 2009 and 2018 for the Sustainable Landscapes Brazil Project. This dataset includes measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Forest_Inventory_Brazil/data- AWS Region
us-west-2
-
- Description
- GEOS_CASAGFED_3H_NEE v2 - This product provides 3 hourly average net ecosystem exchange (NEE) and gross ecosystem exchange (GEE) of Carbon derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GEOS_CASAGFED_3H_NEE.2- AWS Region
us-west-2
-
- Description
- GEOS_CASAGFED_D_FIRE v2 - This product provides Daily average wildfire emissions (FIRE) and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GEOS_CASAGFED_D_FIRE.2- AWS Region
us-west-2
-
- Description
- GEOS_CASAGFED_M_FLUX v2 - This product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GEOS_CASAGFED_M_FLUX.2- AWS Region
us-west-2
-
- Description
- CMS_Global_Fire_Atlas_1642 v1 - The Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Fire_Atlas/data- AWS Region
us-west-2
-
- Description
- GlobFireCarbon v1 - This dataset provides carbon monoxide and carbon dioxide flux from fires constrained by satellite observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GlobFireCarbon.1- AWS Region
us-west-2
-
- Description
- CMS_Global_Forest_AGC_2180 v1 - This dataset provides global gridded estimates of forest aboveground carbon stocks and potential fluxes at a 0.01-degree resolution. It was derived by initializing a newly developed global Ecosystem Demography model (ED v3.0) with novel remote sensing observations of tree canopy height collected by GEDI and ICESat-2, two NASA spaceborne lidar missions.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Forest_AGC/data- AWS Region
us-west-2
-
- Description
- Methane_Flaring_Sites_VIIRS_1874 v1 - This dataset contains annual global flare site surveys from 2012-2019 derived from Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (SNPP) satellite. Gas flaring sites were identified from heat anomalies first estimated by the VIIRS Nightfire (VNF) algorithm from which high-temperature biomass burning and low-temperature gas flaring were separated based on temperature and persistence.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Methane_Flaring_Sites_VIIRS/data- AWS Region
us-west-2
-
- Description
- CMS_Global_Soil_Respiration_1736 v1 - This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Soil_Respiration/data- AWS Region
us-west-2
-
- Description
- SoilResp_HeterotrophicResp_1928 v1 - This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SoilResp_HeterotrophicResp/data- AWS Region
us-west-2
-
- Description
- GFEI_CH4 v1 - This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GFEI_CH4.1- AWS Region
us-west-2
-
- Description
- CMS_Global_Map_Mangrove_Canopy_1665 v1.3 - This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Map_Mangrove_Canopy/data- AWS Region
us-west-2
-
- Description
- CMS_Global_Mangrove_Loss_1768 v1 - This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Mangrove_Loss/data- AWS Region
us-west-2
-
- Description
- CMSGCH4F v1 - This dataset provides global methane fluxes optimized with GOSAT data for 2010-2018. It is supported by the Carbon Monitoring System project.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSGCH4F.1- AWS Region
us-west-2
-
- Description
- Global_Salt_Marsh_Change_2122 v1 - This dataset provides global salt marsh change, including loss and gain for five-year periods from 2000-2019. Loss and gain at a 30 m spatial resolution were estimated with Normalized Difference Vegetation Index (NDVI) anomaly algorithm using Landsat 5, 7, and 8 collections within the known extent of salt marshes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Global_Salt_Marsh_Change/data- AWS Region
us-west-2
-
- Description
- Global_Freshwater_CH4Emissions_2253 v1 - This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Global_Freshwater_CH4Emissions/data- AWS Region
us-west-2
-
- Description
- Tidal_Marsh_Vegetation_US_1608 v1 - This dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Marsh_Vegetation_US/data- AWS Region
us-west-2
-
- Description
- CStocks_Greenness_Mangroves_MX_1853 v1 - This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CStocks_Greenness_Mangroves_MX/data- AWS Region
us-west-2
-
- Description
- Tidal_Wetland_GPP_CONUS_1792 v1 - This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Wetland_GPP_CONUS/data- AWS Region
us-west-2
-
- Description
- SIF_PAR_fPAR_US_Midwest_2018_1813 v1 - This dataset provides estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF under clear-sky and real/cloudy conditions at a resolution of 4 km for the Midwest USA. The estimates are 8-day averaged daily means over the 2018 crop growing season for the time period 2018-05-01 to 2018-09-29.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SIF_PAR_fPAR_US_Midwest_2018/data- AWS Region
us-west-2
-
- Description
- CMS_HR_MNA_CH4_FLUX v1 - This data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_HR_MNA_CH4_FLUX.1- AWS Region
us-west-2
-
- Description
- CMS_Landcover_Indonesia_1838 v1 - This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Landcover_Indonesia/data- AWS Region
us-west-2
-
- Description
- Estimated_Biomass_Stock_Amazon_1648 v1 - This dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Estimated_Biomass_Stock_Amazon/data- AWS Region
us-west-2
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- Description
- CMS_LiDAR_Biomass_MD_PA_DE_1538 v2 - This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Biomass_MD_PA_DE/data- AWS Region
us-west-2
-
- Description
- AGB_CanopyHt_Cover_NewEngland_1854 v1 - This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_CanopyHt_Cover_NewEngland/data- AWS Region
us-west-2
-
- Description
- Forest_AGB_NW_USA_1766 v1 - This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Forest_AGB_NW_USA/data- AWS Region
us-west-2
-
- Description
- LiDAR_Forest_Inventory_Brazil_1644 v1 - This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/LiDAR_Forest_Inventory_Brazil/data- AWS Region
us-west-2
-
- Description
- CMS_LiDAR_AGB_California_1537 v1 - This dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized "remote sensing aware" allometric equation to these individual trees.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_AGB_California/data- AWS Region
us-west-2
-
- Description
- CMS_CH4_FLX_CA v1 - This data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. A related data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CH4_FLX_CA.1- AWS Region
us-west-2
-
- Description
- CMS_CH4_FLX_MX v1 - This data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. A related data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CH4_FLX_MX.1- AWS Region
us-west-2
-
- Description
- CMS_CH4_FLX_NAD v1 - The CMS Methane (CH4) Flux for North America data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The nested approach of the inversion enables large point sources to be resolved while aggregating regions with weak emissions and minimizing aggregation errors.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CH4_FLX_NAD.1- AWS Region
us-west-2
-
- Description
- Methane_Ethane_MA_NH_1982 v1 - This dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Methane_Ethane_MA_NH/data- AWS Region
us-west-2
-
- Description
- CH4_Plume_AVIRIS-NG_1727 v1 - This dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CH4_Plume_AVIRIS-NG/data- AWS Region
us-west-2
-
- Description
- MICASA_FLUX_3H v1 - MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/MICASA_FLUX_3H.1- AWS Region
us-west-2
-
- Description
- SLOPE_GPP_CONUS_1786 v1 - This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SLOPE_GPP_CONUS/data- AWS Region
us-west-2
-
- Description
- NASMo_TiAM_250m_2326 v1 - This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vege...
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/NASMo_TiAM_250m/data- AWS Region
us-west-2
-
- Description
- BGC_glider_GNATS v1 - This dataset contains ocean biogeochemistry data from two Slocum gliders along the Gulf of Maine North Atlantic Time Series (GNATS) transect. The transect runs approximately east-west, with only a very minor change in latitude.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/BGC_glider_GNATS.1- AWS Region
us-west-2
-
- Description
- CMS_OCE_BGC_CCS v1 - A coupled physical-biogeochemical ocean model (the MITgcm with BLING biogeochemistry) is a least squares fit to all available ocean observations in the region of the California Current System. This is accomplished iteratively through the adjoint method, using the methodology developed by the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_OCE_BGC_CCS.1- AWS Region
us-west-2
-
- Description
- CMS_Monthly_CO2_Gulf_1668 v1 - This dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of America for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Monthly_CO2_Gulf/data- AWS Region
us-west-2
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- Description
- Coastal_US_Elevation_Data_1844 v1 - This dataset provides maps of the elevation of coastal wetlands relative to tidal ranges for the conterminous United States (CONUS) at 30 m resolution for 2010. It also includes maps of tidal amplitude, relative sea-level rise for the period 1983-2001, and maps for coastal lands and low marsh areas based on the probability of being below the mean higher high tide water line for spring tides (MHHWS).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Coastal_US_Elevation_Data/data- AWS Region
us-west-2
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- Description
- CMS_Coastal_Wetland_Resilience_1839 v1 - This dataset provides information about the resilience of tidal wetlands to sea-level rise under three scenarios of global change. With rising seas, regularly inundated tidal wetlands may persist by vertical accretion of sediments (vertical resilience) and/or by migrating inland (lateral resilience), but local and regional conditions constrain these options.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Coastal_Wetland_Resilience/data- AWS Region
us-west-2
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- Description
- SatelliteDerived_Forest_Mexico_2320 v1 - This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SatelliteDerived_Forest_Mexico/data- AWS Region
us-west-2
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- Description
- SiB4_Global_HalfDegree_Daily_1849 v1 - This dataset provides global daily output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SiB4_Global_HalfDegree_Daily/data- AWS Region
us-west-2
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- Description
- SiB4_Global_HalfDegree_Hourly_1847 v1 - This dataset provides global hourly output predicted from the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SiB4_Global_HalfDegree_Hourly/data- AWS Region
us-west-2
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- Description
- SiB4_Global_HalfDegree_Monthly_1848 v1 - This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/SiB4_Global_HalfDegree_Monthly/data- AWS Region
us-west-2
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- Description
- Crops_SIF_VegIndices_IL_NE_2136 v1 - This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Crops_SIF_VegIndices_IL_NE/data- AWS Region
us-west-2
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- Description
- Wetland_Soil_CarbonStocks_WA_2249 v1 - This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Wetland_Soil_CarbonStocks_WA/data- AWS Region
us-west-2
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- Description
- CMS_SOC_Mexico_1754 v1 - This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SOC_Mexico/data- AWS Region
us-west-2
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- Description
- CMS_SOC_Mexico_CONUS_1737 v1 - This dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SOC_Mexico_CONUS/data- AWS Region
us-west-2
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- Description
- Country_SOC_Latin_America_1615 v1 - This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Country_SOC_Latin_America/data- AWS Region
us-west-2
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- Description
- Tidal_Wetland_Soil_Carbon_1612 v1 - This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Wetland_Soil_Carbon/data- AWS Region
us-west-2
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- Description
- Tree_Canopy_Cover_Mexico_2137 v1 - The data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022).
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tree_Canopy_Cover_Mexico/data- AWS Region
us-west-2
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- Description
- High_Res_Tidal_Marsh_Veg_1609 v1 - This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/High_Res_Tidal_Marsh_Veg/data- AWS Region
us-west-2
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- Description
- Wetland_Salinity_Maps_2392 v1 - This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis.
- Resource type
- S3 Bucket Controlled Access
- Amazon Resource Name (ARN)
arn:aws:s3:::ornl-cumulus-prod-protected/cms/Wetland_Salinity_Maps/data- AWS Region
us-west-2