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agriculturedisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.
agriculturecogdisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
This joint NASA/USGS program provides the longest continuous space-based record of
Earth’s land in existence. Every day, Landsat satellites provide essential information
to help land managers and policy makers make wise decisions about our resources and our environment.
Data is provided for Landsats 1, 2, 3, 4, 5, 7, 8, and 9 (excludes Landsat 6).As of June 28, 2023 (announcement),
the previous single SNS topic arn:aws:sns:us-west-2:673253540267:public-c2-notify was replaced with
three new SNS topics for different types of scenes.
agricultureair qualityanalyticsarchivesatmosphereclimateclimate modeldata assimilationdeep learningearth observationenergyenvironmentalforecastgeosciencegeospatialglobalhistoryimagingindustrymachine learningmachine translationmetadatameteorologicalmodelnetcdfopendapradiationsatellite imagerysolarstatisticssustainabilitytime series forecastingwaterweatherzarr
NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.
The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.
The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.
The latest data version update includes hourly...
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.
NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the [GOES-R ABI Reprocess User Guide](https://github.com/NOAA-Big-Data-Program/nodd-data-docs/blob/main/GOES/GOES-R_ABI_Reprocessed_L1b_User_Guide-v1.1.pdf).
NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.
GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and
monitoring of meteorological and space environment data across North America.
GO...
agriculturecogdisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in ongoing studies. This dataset is the same as the Sentinel-2 dataset, except the JP2K files were converted into Cloud-Optimized GeoTIFFs (COGs). Additionally, SpatioTemporal Asset Catalog metadata has were in a JSON file alongside the data, and a STAC API called Earth-search is freely available t...
cogearth observationgeophysicsgeospatialglobalicenetcdfsatellite imagerystaczarr
The Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project has a singular mission: to accelerate ice sheet and glacier research by producing globally comprehensive, high resolution, low latency, temporally dense, multi-sensor records of land ice and ice shelf change while minimizing barriers between the data and the user. ITS_LIVE data currently consists of NetCDF Level 2 scene-pair ice flow products posted to a standard 120 m grid derived from Landsat 4/5/7/8/9, Sentinel-2 optical scenes, and Sentinel-1 SAR scenes. We have processed all land-ice intersecting image pai...
agriculturecogdisaster responseearth observationgeospatialland coverland usemachine learningmappingnatural resourcesatellite imagerystacsustainabilitysynthetic aperture radar
The European Space Agency (ESA) WorldCover product provides global land cover maps for 2020 & 2021 at 10 m resolution based on Copernicus Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency. A first version of the product (v100), containing the 2020 map was released in October 2021. The 2021 map was released in October 2022 using an improved algorithm (v200). The WorldCover 2020 and 2021 maps we...
computer visiondisaster responseearth observationgeospatialmachine learningsatellite imagery
SpaceNet, launched in August 2016 as an open innovation project offering a repository of freely available imagery with co-registered map features. Before SpaceNet, computer vision researchers had minimal options to obtain free, precision-labeled, and high-resolution satellite imagery. Today, SpaceNet hosts datasets developed by its own team, along with data sets from projects like IARPA’s Functional Map of the World (fMoW).
coastalcogdeafricaearth observationgeospatialland covernatural resourcesatellite imagerystacsustainability
The Global Mangrove Watch (GMW) dataset is a result of the collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). The primary objective of producing this dataset is to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation. The Global Mangrove Watch dataset (version 2) consists of a global baseline map of ...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
Digital Earth Africa (DE Africa) provides free and open access to a copy of Landsat Collection 2 Level-2 products over Africa. These products are produced and provided by the United States Geological Survey (USGS). The Landsat series of Earth Observation satellites, jointly led by USGS and NASA, have been continuously acquiring images of the Earth’s land surface since 1972. DE Africa provides data from Landsat 5, 7 and 8 satellites, including historical observations dating back to late 1980s and regularly updated new acquisitions. New Level-2 Landsat 7 and Landsat 8 data are available after 15...
agriculturecogdisaster responseearth observationgeospatialglobalicesatellite imagerysynthetic aperture radar
Developed and operated by the Canadian Space Agency, it is Canada's first commercial Earth observation satellite Développé et exploité par l'Agence spatiale canadienne, il s'agit du premier satellite commercial d'observation de la Terre au Canada.
agricultureclimatecogdeafricaearth observationfood securitygeospatialmeteorologicalsatellite imagerystacsustainability
Digital Earth Africa (DE Africa) provides free and open access to a copy of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) monthly and daily products over Africa. The CHIRPS rainfall maps are produced and provided by the Climate Hazards Center in collaboration with the US Geological Survey, and use both rain gauge and satellite observations. The CHIRPS-2.0 Africa Monthly dataset is regularly indexed to DE Africa from the CHIRPS monthly data. The CHIRPS-2.0 Africa Daily dataset is likewise indexed from the CHIRPS daily data. Both products have been converted to clou...
climatecoastaldeafricaearth observationgeospatialsatellite imagerysustainability
Africa's long and dynamic coastline is subject to a wide range of pressures, including extreme weather and climate, sea level rise and human development. Understanding how the coastline responds to these pressures is crucial to managing this region, from social, environmental and economic perspectives. The Digital Earth Africa Coastlines (provisional) is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline from 2000 to the present. The product combines satellite data from the Digital Earth Africa program with tidal modelling t...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop m...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacwater
Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite. They are generated using the WOfS classification algorithm on Landsat satellite data. There are several WOfS products available for the African continent including scene-level dat...
cogdisaster responseearth observationgeospatialsatellite imagerystac
Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated using the Maxar ARD pipeline, tiled on an organized grid in analysis-ready cloud-optimized formats.
agriculturecogdisaster responseearth observationgeospatialimagingsatellite imagerystac
Imagery acquired by the China-Brazil Earth Resources Satellite (CBERS), 4 and 4A. The image files are recorded and processed by Instituto Nacional de Pesquisas Espaciais (INPE) and are converted to Cloud Optimized Geotiff format in order to optimize its use for cloud based applications. Contains all CBERS-4 MUX, AWFI, PAN5M and PAN10M scenes acquired since the start of the satellite mission and is daily updated with new scenes. CBERS-4A MUX Level 4 (Orthorectified) scenes are being ingested starting from 04-13-2021. CBERS-4A WFI Level 4 (Orthorectified) scenes are being ingested starting from ...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsynthetic aperture radar
DE Africa’s Sentinel-1 backscatter product is developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specifications. The Sentinel-1 mission, composed of a constellation of two C-band Synthetic Aperture Radar (SAR) satellites, are operated by European Space Agency (ESA) as part of the Copernicus Programme. The mission currently collects data every 12 days over Africa at a spatial resolution of approximately 20 m. Radar backscatter measures the amount of microwave radiation reflected back to the sensor from the ground surface. This measurement is sensitive to surface rough...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product. Together, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days. Sentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data fr...
climateearth observationenvironmentalnatural resourceoceanssatellite imagerywaterweather
A global, gap-free, gridded, daily 1 km Sea Surface Temperature (SST) dataset created by merging multiple Level-2 satellite SST datasets. Those input datasets include the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 (AMSR-2) on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. Data are available fro...
aerial imagerycogearth observationgeospatialsatellite imagerystac
The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zea...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsynthetic aperture radar
The ALOS/PALSAR annual mosaic is a global 25 m resolution dataset that combines data from many images captured by JAXA’s PALSAR and PALSAR-2 sensors on ALOS-1 and ALOS-2 satellites respectively. This product contains radar measurement in L-band and in HH and HV polarizations. It has a spatial resolution of 25 m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2020 (ALOS-2/PALSAR-2). The JERS annual mosaic is generated from images acquired by the SAR sensor on the Japanese Earth Resources Satellite-1 (JERS-1) satellite. This product contains radar measurement in L-band and H...
agriculturecogdeafricaearth observationfood securitygeospatialsatellite imagerystacsustainability
Digital Earth Africa's cropland extent map (2019) shows the estimated location of croplands in Africa for the period January to December 2019. Cropland is defined as: "a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvest-able at least once within the 12 months after the sowing/planting date." This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. This provisional cropland extent map has a resolution of 10m, and was built using Cope...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsustainability
Fractional cover (FC) describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The product is derived from Landsat satellite data, using an algorithm developed by the Joint Remote Sensing Research Program. Digital Earth Africa's FC service has two components. Fractional Cover is estimated from each Landsat scene, providing measurements from individual days. Fractional Cover...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
Digital Earth Africa’s Monthly NDVI Anomaly service provides estimate of vegetation condition, for each caldendar month, against the long-term baseline condition measured for the month from 1984 to 2020 in the NDVI Climatology. A standardised anomaly is calculated by subtracting the long-term mean from an observation of interest and then dividing the result by the long-term standard deviation. Positive NDVI anomaly values indicate vegetation is greener than average conditions, and are usually due to increased rainfall in a region. Negative values indicate additional plant stress relative to t...
cogdisaster responseearth observationsatellite imagerystac
Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized GeoTIFF format (COG) and search using the Spatial Temporal Asset Catalog (STAC) standard. The data is...
cogearth observationelevationgeospatialmappingopen source softwaresatellite imagerystac
ArcticDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2007 to the present. The ArcticDEM project seeks to fill the need for high-resolution time-series elevation data in the Arctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. ArcticDEM data is constructed from in-track and cross-track high...
agricultureagriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
Digital Earth Africa’s NDVI climatology product represents the long-term average baseline condition of vegetation for every Landsat pixel over the African continent. Both mean and standard deviation NDVI climatologies are available for each calender month.Some key features of the product are:
cogearth observationenvironmentalgeospatiallabeledmachine learningsatellite imagerystac
Radiant MLHub is an open library for geospatial training data that hosts datasets generated by Radiant Earth Foundation's team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog and exposed through a common API. Training datasets include pairs of imagery and labels for different types of machine learning problems including image ...
cogearth observationelevationgeospatialmappingopen source softwaresatellite imagerystac
The Reference Elevation Model of Antarctica - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2009 to the present. The REMA project seeks to fill the need for high-resolution time-series elevation data in the Antarctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. REMA data is constructed from in...
cogearth observationgeospatialminingnatural resourcesatellite imagerysustainability
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B), that has been geometrically corrected, and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) product.The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytim...
cogearth observationgeosciencegeospatialimage processingopen source softwaresatellite imagerystac
Earth observation (EO) data cubes produced from analysis-ready data (ARD) of CBERS-4, Sentinel-2 A/B and Landsat-8 satellite images for Brazil. The datacubes are regular in time and use a hierarchical tiling system. Further details are described in Ferreira et al. (2020).
aerial imagerycogdisaster responseearth observationsatellite imagery
OpenAerialMap is a collection of high-resolution openly licensed satellite and aerial imagery.
cogearth observationenvironmentalgeospatialland coverland usemachine learningmappingplanetarysatellite imagerystacsustainability
This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to ...
cogcomputer visionearth observationgeospatialimage processingsatellite imagerystacsynthetic aperture radar
Open Synthetic Aperture Radar (SAR) data from Capella Space. Capella Space is an information services company that provides on-demand, industry-leading, high-resolution synthetic aperture radar (SAR) Earth observation imagery. Through a constellation of small satellites, Capella provides easy access to frequent, timely, and flexible information affecting dozens of industries worldwide. Capella's high-resolution SAR satellites are matched with unparalleled infrastructure to deliver reliable global insights that sharpen our understanding of the changing world – improving decisions ...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacwater
The Digital Earth Africa continental Waterbodies Monitoring Service identifies more than 700,000 water bodies from over three decades of satellite observations. This service maps persistent and seasonal water bodies and the change in their water surface area over time. Mapped water bodies may include, but are not limited to, lakes, ponds, man-made reservoirs, wetlands, and segments of some river systems.On a local, regional, and continental scale, this service helps improve our understanding of surface water dynamics and water availability and can be used for monitoring water bodies such as we...
cogearth observationelevationgeospatialmappingopen source softwaresatellite imagerystac
EarthDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2002 to the present. The EarthDEM project seeks to fill the need for high-resolution time-series elevation data in non-polar regions. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. EarthDEM data is constructed from in-track and cross-track ...
computer visiondeep learningearth observationgeospatiallabeledmachine learningsatellite imagery
RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very high resolution dataset built to test the value of synthetic data from an overhead perspective. The real portion ...
agriculturecogdisaster responseearth observationgeospatialimagingsatellite imagerystacsustainability
Imagery acquired by Amazonia-1 satellite. The image files are recorded and processed by Instituto Nacional de Pesquisas Espaciais (INPE) and are converted to Cloud Optimized Geotiff format in order to optimize its use for cloud based applications. WFI Level 4 (Orthorectified) scenes are being ingested daily starting from 08-29-2022, the complete Level 4 archive will be ingested by the end of October 2022.
agriculturecogdisaster responseearth observationgeospatialland coverland usemachine learningmappingnatural resourcesatellite imagerystacsustainabilitysynthetic aperture radar
The WorldCover 10m Annual Composites were produced, as part of the European Space Agency (ESA) WorldCover project, from the yearly Copernicus Sentinel-1 and Sentinel-2 archives for both years 2020 and 2021. These global mosaics consists of four products composites. A Sentinel-2 RGBNIR yearly median composite for bands B02, B03, B04, B08. A Sentinel-2 SWIR yearly median composite for bands B11 and B12. A Sentinel-2 NDVI yearly percentiles composite (NDVI 90th, NDVI 50th NDVI 10th percentiles). A Sentinel-1 GAMMA0 yearly median composite for bands VV, VH and VH/VV (power scaled). Each product is...
agriculturecogdisaster responseelevationgeospatialhydrologysatellite imagerystac
Height Above Nearest Drainage (HAND) is a terrain model that normalizes topography to the relative heights along the drainage network and is used to describe the relative soil gravitational potentials or the local drainage potentials. Each pixel value represents the vertical distance to the nearest drainage. The HAND data provides near-worldwide land coverage at 30 meters and was produced from the 2021 release of the Copernicus GLO-30 Public DEM as distributed in the Registry of Open Data on AWS.
agriculturecogearth observationearthquakesecosystemsenvironmentalgeologygeophysicsgeospatialglobalinfrastructuremappingnatural resourcesatellite imagerysynthetic aperture radarurban
This data set is the first-of-its-kind spatial representation of multi-seasonal, global SAR repeat-pass interferometric coherence and backscatter signatures. Global coverage comprises all land masses and ice sheets from 82 degrees northern to 79 degrees southern latitude. The data set is derived from high-resolution multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 Single-Look-Complex data acquired in Interferometric Wide-Swath mode (Sentinel-1 IW mode) from 1-Dec-2019 to 30-Nov-2020. The data set was developed by Earth Big Data LLC and Gamma Remote Sensing AG, under contract for NASA's Jet Propulsion Laboratory. ...
agriculturecogdeep learninglabeledland covermachine learningsatellite imagery
High resolution, annual cropland and landcover maps for selected African countries developed by Clark University's Agricultural Impacts Research Group using various machine learning approaches applied to Planet imagery, including field boundary and cultivated frequency maps, as well as multi-class land cover.
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
Himawari-9, stationed at 140.7E, owned and operated by the Japan Meteorological Agency (JMA), is a geostationary meteorological satellite, with Himawari-8 as on-orbit back-up, that provides constant and uniform coverage of east Asia, and the west and central Pacific regions from around 35,800 km above the equator with an orbit corresponding to the period of the earth’s rotation. This allows JMA weather offices to perform uninterrupted observation of environmental phenomena such as typhoons, volcanoes, and general weather systems. Archive data back to July 2015 is available for Full Disk (AHI-L...
earth observationgeospatialsatellite imageryurban
NDUI is combined with cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution,and it can greatly enhance urban areas, which can then be easily distinguished from bare lands including fallows and deserts. With the capability to delineate urban boundaries and, at the same time, to present sufficient spatial details within urban areas, the NDUI has the potential for urbanization studies at regional and global scales.
air qualityatmosphereearth observationenvironmentalgeospatialsatellite imagery
NO2 tropospheric column density, screened for CloudFraction < 30% global daily composite at 0.25 degree resolution for the temporal range of 2004 to May 2020. Original archive data in HDF5 has been processed into a Cloud-Optimized GeoTiff (COG) format. Quality Assurance - This data has been validated by the NASA Science Team at Goddard Space Flight Center.Cautionary Note: https://airquality.gsfc.nasa.gov/caution-interpretation.
agriculturecoglabeledland covermachine learningsatellite imagery
Crop field boundaries digitized in Planet imagery collected across Africa between 2017 and 2023, developed by Farmerline, Spatial Collective, and the Agricultural Impacts Research Group at Clark University, with support from the Lacuna Fund (Estes et al, 2024; Details →
atmosphereclimatedeep learningenvironmentalexplorationgeophysicsgeosciencegeospatialglobaliceplanetarysatellite imageryzarr
The Chalmers Cloud Ice Climatology (CCIC) is a novel, deep-learning-based climate record of ice-particle concentrations in the atmosphere. CCIC results are available at high spatial and temporal resolution (0.07° / 3 h from 1983, 0.036° / 30 min from 2000) and thus ideally suited for evaluating high-resolution weather and climate models or studying individual weather systems.
aerial imageryagricultureclimatecogearth observationgeospatialimage processingland covermachine learningsatellite imagery
Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.
cogplanetarysatellite imagerystac
The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data were collected in monoscopic observing mode. To cre...
cogplanetarysatellite imagerystac
Knowledge of a planetary surface’s topography is necessary to understand its geology and enable landed mission operations. The Solid State Imager (SSI) on board NASA’s Galileo spacecraft acquired more than 700 images of Jupiter’s moon Europa. Although moderate- and high-resolution coverage is extremely limited, repeat coverage of a small number of sites enables the creation of digital terrain models (DTMs) via stereophotogrammetry. Here we provide stereo-derived DTMs of five sites on Europa. The sites are the bright band Agenor Linea, the crater Cilix, the crater Pwyll, pits and chaos adjacent...
cogplanetarysatellite imagerystac
These data are infrared image mosaics, tiled to the Mars quadrangle, generated using Thermal Emission Imaging System (THEMIS) images from the 2001 Mars Odyssey orbiter mission. The mosaic is generated at the full resolution of the THEMIS infrared dataset, which is approximately 100 meters/pixel. The mosaic was absolutely photogrammetrically controlled to an improved Viking MDIM network that was develop by the USGS Astrogeology processing group using the Integrated Software for Imagers and Spectrometers. Image-to-image alignment precision is subpixel (i.e., <100m). These 8-bit, qualitative d...
cogplanetarysatellite imagerystac
The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 92 image mosaics generated from minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface.
These images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The S...
cogplanetarysatellite imagerystac
The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 481 minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface. These individual images were subsequently used as input into a set of 92 observation mosaics.
These images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface).
The dataset provides a set of individual images that can be used for scientific analysis...
cogelevationplanetarysatellite imagerystac
As of March, 2023 the Mars Reconnaissance Orbiter (MRO) High Resolution Science Experiment (HiRISE) sensor has collected more than 5000 targeted stereopairs. During HiRISE acquisition, the Context Camera (CTX) also collects lower resolution, higher spatial extent context images. These CTX acquisitions are also targeted stereopairs. This data set contains targeted CTX DTMs and orthoimages, created using the NASA Ames Stereopipeline. These data have been created using relatively controlled CTX images that have been globally bundle adjusted using the USGS Integrated System for Imagers and Spectro...
cogplanetarysatellite imagerystac
These data are digital terrain models (DTMs) created by multiple different institutions and released to the Planetary Data System (PDS) by the University of Arizona. The data are processed from the Planetary Data System (PDS) stored JP2 files, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are controlled to the Mars Orbiter Laser Altimeter (MOLA). Therefore, they are a proxy for the geodetic coordinate reference frame. These data are not guaranteed to co-register with an uncontrolled products (e.g., the uncontrolled High Resolution ...
cogplanetarysatellite imagerystac
These data are red and color Reduced Data Record (RDR) observations collected and originally processed by the High Resolution Imaging Science Experiment (HiRISE) team. The mdata are processed from the Planetary Data System (PDS) stored RDRs, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are not photogrammetrically controlled and use a priori NAIF SPICE pointing. Therefore, these data will not co-register with controlled data products. Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180...
agricultureanalysis ready dataceosdisaster responseearth observationgeospatialsatellite imagerystacsustainabilitysynthetic aperture radar
The RADARSAT Constellation Mission (RCM) is Canada's third generation of Earth observation satellites. Launched on June 12, 2019, the three identical satellites work together to bring solutions to key challenges for Canadians. As part of ongoing Open Government efforts, NRCan produces a CEOS analysis ready data (ARD) of Canada landmass using a 30M Compact-Polarization standard coverage, every 12 days. RCM CEOS-ARD (POL) is the first ever polarimetric dataset approved by the CEOS committee. Previously, users were stuck ordering, downloading and processing RCM images (level 1) on their own, often with expensive software. This new dataset aims to remove these burdens with a new STAC catalog for discovery and direct download links.
La mission de la Constellation RADARSAT (MCR) est la troisième génération de satellites d'observation de la Terre du Canada. Lancés le 12 juin 2019, les trois satellites identiques travaillent ensemble pour apporter des solutions aux principaux défis des Canadiens. Dans le cadre des efforts continus pour un gouvernement ouvert, RNCan produit des données prêtes à l'analyse CEOS (ARD) de la masse terrestre du Canada en utilisant une couverture standard de 30 m en polarisation compacte, tous les 12 jours. Les CEOS-ARD (POL) du MCR constituent le premier ensemble de données polarimétriques jamais approuvé par le comité CEOS. Auparavant, les utilisateurs étaient obligés de commander, de télécharger...
agriculturecogdisaster responseearth observationgeospatialsatellite imagerysynthetic aperture radar
Sentinel-1 is a pair of European radar imaging (SAR) satellites launched in 2014 and 2016. Its 6 days revisit cycle and ability to observe through clouds makes it perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.
agriculturecogearth observationgeospatialmachine learningnatural resourcesatellite imagery
Sentinel-2 L2A 120m mosaic is a derived product, which contains best pixel values for 10-daily periods, modelled by removing the cloudy pixels and then performing interpolation among remaining values. As there are some parts of the world, which have lengthy cloudy periods, clouds might be remaining in some parts. The actual modelling script is available here.
cogearth observationenvironmentalgeospatiallandoceanssatellite imagerystac
This data set consists of observations from the Sentinel-3 satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-3 is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the Ocean and Land Colour Instrument (OLCI) for medium resolution marine and terrestrial optical measurements, the Sea and Land Surface Temperature Radiometer (SLSTR), the SAR Radar Altimeter (SRAL), the MicroWave Radiometer (MWR) and the Precise Orbit Determination (POD) instruments. The satellite was launched in 2016 and entered routine operational phase in 201...
air qualityatmospherecogearth observationenvironmentalgeospatialsatellite imagerystac
This data set consists of observations from the Sentinel-5 Precursor (Sentinel-5P) satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen dioxide and sulphur dioxide in the atmosphere. The satellite was launched in October 2017 and entered ro...
meteorologicalsatellite imageryweather
Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.
agricultureanalyticsbiodiversityconservationdeep learningfood securitygeospatialmachine learningsatellite imagery
iSDAsoil is a resource containing soil property predictions for the entire African continent, generated using machine learning. Maps for over 20 different soil properties have been created at 2 different depths (0-20 and 20-50cm). Soil property predictions were made using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples. Included in this dataset are images of predicted soil properties, model error and satellite covariates used in the mapping process.
cogdisaster responsegeospatialsatellite imagerystac
synthetic Aperture Radar (SAR) data is a powerful tool for monitoring and assessing disaster events and can provide valuable insights for researchers, scientists, and emergency response teams. The Alaska Satellite Facility (ASF) curates this collection of (primarily) SAR and SAR-derived satellite data products from a variety of data sources for disaster events.
climateenvironmentalsatellite imagery
A dataset of satellite retrievals of atmospheric methane that extends from 30 April 2018 to present.
archivesastronomydatacenterimagingsatellite imageryx-ray
NASA data for high energy astrophysics (generally x-ray and gamma-ray domains) is made available here by the High Energy Astrophysics Science Archive Research Center. The HEASARC hosts the full data archives of over 30 different missions spanning 50 years. The data archive for each mission will contain a range of data types from spacecraft housekeeping and raw photon event list data up to high level science-ready products such as images, light curves (time series), and energy spectra.
This is a relatively modest total data volume but contains significant complexity and heterogeneity among the different missions. Data provided here are stored in the Flexible Image Transport System (FITS) format common in astronomy. Higher level products are further defined to be consistent between missions following data model standards agreed by the community and maintained by the HEASARC. Analysis of these data may require software also provided by HEASARC, the HEASoft package, consisting of tools generic to all FITS data, generic to all HEASARC-compliant data, and/or specific to individual missions as appropriate. Some missions provide standard science-ready data products, while others provide low-level data types and software to generate science-ready products from them. See the links for each mission for more information on how to use the data.
The HEASARC Website also has archive browsing tools where you can query for observations corresponding to temporal and spatial constraints among others. These tools will ultimately point to files located on the archive by giving a URL beginning with the path https://heasarc.gsfc.nasa.gov/FTP/. The data that are provided in the ODR follow the same structure, so when our tools give an https access URL, a user can simply swap in s3://nasa-heasarc/ for the first part of that URL and get a cloud URI. Note also that some selections have been made to what has been copied to the ODR, while the HEASARC archive itself remains the definitive and legacy source for the complete datasets.
The HEASARC also...
archivesastronomydatacenterimagingsatellite imagery
NASA data for cosmic microwave background (CMB) analysis is made available here by the Legacy Archive for Microwave Background Data Analysis (LAMBDA), which is a part of NASA's High Energy Astrophysics Science Archive Research Center (HEASARC). LAMBDA hosts the data archives of over 30 different CMB missions spanning 30+ years. The data archive for each mission may contain a range of data types from low-level time-ordered data to high level science-ready products such as sky maps and angular power spectra. Also provided in consistent formats are a variety of full sky maps in complementary ...
cogearth observationgeospatialnatural resourcesatellite imagerywater
Aquatic reflectance produced with the dark spectrum fitting (DSF) algorithm as implemented in the Atmospheric Correction for OLI “lite” (ACOLITE) software (version 20221114.0). Aquatic reflectance is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by pi.
agriculturecogdisaster responseearth observationelevationgeospatialsatellite imagery
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-90 provides worldwide coverage at 90 meters. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that in both cases ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized Ge...
anomaly detectionclassificationdisaster responseearth observationenvironmentalNASA SMD AIsatellite imagerysocioeconomicurban
Detection of nighttime combustion (fire and gas flaring) from daily top of atmosphere data from NASA's Black Marble VNP46A1 product using VIIRS Day/Night Band and VIIRS thermal bands.
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsustainabilitysynthetic aperture radar
JAXA has responded to the Earthquake events in Turkey and Syria by conducting emergency disaster observations and providing data as requested by the Disaster and Emergency Management Authority (AFAD), Ministry of Interior in Turkey, through Sentinel Asia and the International Disaster Charter. Additional information on the event and dataset can be found here. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught...
cogcomputer visionearth observationgeospatialimage processingsatellite imagerystac
Satellogic EarthView dataset includes high-resolution satellite images captured over all continents. The dataset is organized in Hive partition format and hosted by AWS. The dataset can be accessed via STAC browser or aws cli. Each item of the dataset corresponds to a specific region and date, with some of the regions revisited for additional data. The dataset provides Top-of-Atmosphere (TOA) reflectance values across four spectral bands (Red, Green, Blue, Near-Infrared) at a Ground Sample Distance (GSD) of 1 meter, accompanied by comprehensive metadata such as off-nadir angles, sun elevation,...
biodiversityclimatecoastalearth observationenvironmentalgeospatialglobalmachine learningmappingnatural resourcesatellite imagerysustainability
A collection of multi-resolution satellite images from both public and commercial satellites. The dataset is specifically curated for training geospatial foundation models.
disaster responseearth observationenvironmentalgeospatialsatellite imagerysynthetic aperture radar
The S1 Single Look Complex (SLC) dataset contains Synthetic Aperture Radar (SAR) data in the C-Band wavelength. The SAR sensors are installed on a two-satellite (Sentinel-1A and Sentinel-1B) constellation orbiting the Earth with a combined revisit time of six days, operated by the European Space Agency. The S1 SLC data are a Level-1 product that collects radar amplitude and phase information in all-weather, day or night conditions, which is ideal for studying natural hazards and emergency response, land applications, oil spill monitoring, sea-ice conditions, and associated climate change effec...
geospatialsatellite imagery
The Terra Basic Fusion dataset is a fused dataset of the original Level 1 radiances from the five Terra instruments. They have been fully validate to contain the original Terra instrument Level 1 data. Each Level 1 Terra Basic Fusion file contains one full Terra orbit of data and is typically 15 – 40 GB in size, depending on how much data was collected for that orbit. It contains instrument radiance in physical units; radiance quality indicator; geolocation for each IFOV at its native resolution; sun-view geometry; bservation time; and other attributes/metadata. It is stored in HDF5, conformed to CF conventions, and accessible by netCDF-4 enhanced models. It’s naming convention follows: TERRA_BF_L1B_OXXXX_YYYYMMDDHHMMSS_F000_V000.h5. A concise description of the dataset, along with links to complete documentation and available software tools, can be found on the Terra Fusion project page: https://terrafusion.web.illinois.edu.Terra is the flagship satellite of NASA’s Earth Observing System (EOS). It was launched into orbit on December 18, 1999 and carries five instruments. These are the Moderate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Clouds and Earth’s Radiant Energy System (CERES), and the Measurements of Pollution in the Troposphere (MOPITT).The Terra Basic Fusion dataset is an easy-to-access record of the Level 1 radiances for instruments on...
atmosphereearth observationenvironmentalgeophysicsgeoscienceglobalmeteorologicalmodelnetcdfprecipitationsatellite imageryweather
The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Dataset (TC PRIMED) is a dataset centered around passive microwave observations of global tropical cyclones from low-Earth-orbiting satellites. TC PRIMED is a compilation of tropical cyclone data from various sources, including 1) tropical cyclone information from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service National Hurricane Center (NHC) and Central Pacific Hurricane Center (CPHC) and the U.S. Department of Defense Joint Typhoon Warning Center, 2) low-Earth-orbiting satellite obse...
astronomyimagingsatellite imagerysurvey
The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The 3-Band Cryo Data Release contains 3.4, 4.6 and 12 micron (W1, W2, W3) imaging data that were acquired between 6 Aug and 29 Sept 2010 while the detectors were cooled by the inner cryogen tank following the exhaustion of the outer tank.
astronomyimagingsatellite imagerysurvey
The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The All-Sky Release includes all data taken during the WISE full cryogenic mission phase, 7 January 2010 to 6 August 2010, in the 3.4, 4.6, 12, and 22 micron bands (i.e., W1, W2, W3, W4) that were processed with improved calibrations and reduction algorithms.
astronomyimagingobject detectionparquetsatellite imagerysurvey
The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The AllWISE Data Release combines data from all cryogenic and post-cryogenic survey phases and provides a comprehensive view of the mid-infrared sky. The Images Atlas includes 18,240 FITS image sets at 3.4, 4.6, 12 and 22 microns. The Source Catalog contains position, apparent motion, and flux information for over 747 million objects detected on the Atlas Images.
agriculturecogdisaster responseearth observationenvironmentalgeospatialsatellite imagerystacsynthetic aperture radar
The Sentinel-1 mission is a constellation of C-band Synthetic Aperature Radar (SAR) satellites from the European Space Agency launched since 2014. These satellites collect observations of radar backscatter intensity day or night, regardless of the weather conditions, making them enormously valuable for environmental monitoring. These radar data have been processed from original Ground Range Detected (GRD) scenes into a Radiometrically Terrain Corrected, tiled product suitable for analysis. This product is available over the Contiguous United States (CONUS) since 2017 when Sentinel-1 data becam...
astronomymachine learningNASA SMD AIsatellite imagery
Hubble Space Telescope imaging data and associated identification labels for galaxy morphology derived from citizen scientist labels from the Galaxy Zoo: Hubble project.
aerial imagerydemographicsdisaster responsegeospatialimage processingmachine learningpopulationsatellite imagery
Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV and Cloud-optimized GeoTIFF files. This refines CIESIN’s Gridded Population of the World using machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census data are allocated to blocks where imagery appears to contain buildings.
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
The Geo-KOMPSAT-2A (GK2A) is the new generation geostationary meteorological satellite (located in 128.2°E) of the Korea Meteorological Administration (KMA). The main mission of the GK2A is to observe the atmospheric phenomena over the Asia-Pacific region. The Advance Meteorological Imager (AMI) on GK2A scan the Earth full disk every 10 minutes and the Korean Peninsula area every 2 minutes with a high spatial resolution of 4 visible channels and 12 infrared channels. In addition, the AMI has an ability of flexible target area scanning useful for monitoring severe weather events such as typhoon...
agriculturedisaster responsegeospatialnatural resourcesatellite imagery
Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), managed by the U.S. Geological Survey and NASA. Five products are included: MCD43A4 (MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid), MOD11A1 (MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid), MYD11A1 (MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid), MOD13A1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid), and MYD13A1 (MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid). MCD43A4 has global coverage, all...
astronomyimagingsatellite imagerysurvey
The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The NEOWISE Post-Cryo Data Release contains 3.4 and 4.6 micron (W1 and W2) imaging data that were acquired between 29 September 2010 and 1 February 2011 following the exhaustion of the inner and outer cryogen tanks.
astronomyimagingobject detectionparquetsatellite imagerysurvey
The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) is a NASA Medium-class Explorer satellite in low-Earth orbit conducting an all-sky astronomical imaging survey over two infrared bands. The NEOWISE Reactivation mission began in 2013 when the original WISE satellite was brought out of hibernation to learn more about the population of near-Earth objects and comets that could pose an impact hazard to the Earth. The data is also used to study a wide range of astrophysical phenomena in the time domain including brown dwarfs, supernovae and active galactic nuclei.
astronomyimagingobject detectionparquetsatellite imagerysimulationssurvey
This release consists of simulated data products designed to mimic observations of the same region of the sky as seen by two astronomical facilities: the Nancy Grace Roman Telescope and the Vera C. Rubin Observatory.
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsustainabilitysynthetic aperture radar
The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. The SAR imagery was ortho-rectificatied and slope corrected using the ALOS World 3D - 30 m (AW3D30) Digital Surface Model. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ0 = 10*log10(DN2) - 83.0 dB CARD4L stands for CEOS Analysis Ready Data for Land (Level 2.2) data are ortho-rectified and radiometrically terrain-corrected. This datase...
agriculturecogdeafricadisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsustainabilitysynthetic aperture radar
Torrential rainfall triggered flooding and landslides in many parts of Rwanda. The hardest-hit districts were Ngororero, Rubavu, Nyabihu, Rutsiro and Karongi. According to reports, 14 people have died in Karongi, 26 in Rutsiro, 18 in Rubavu, 19 in Nyabihu and 18 in Ngororero.Rwanda National Police reported that the Mukamira-Ngororero and Rubavu-Rutsiro roads are impassable due to flooding and landslide debris. UNITAR on behalf of United Nations Office for the Coordination of Humanitarian Affairs (OCHA) / Regional Office for Southern & Eastern Africa in cooperation with Rwanda Space Agency ...
agriculturecogdisaster responseearth observationgeospatialnatural resourcesatellite imagerystacsustainabilitysynthetic aperture radar
Tropical Cyclone Mocha began to form in the Bay of Bengal on 11 May 2023 and continues to intensify as it moves towards Myanmar and Bangladesh.Cyclone Mocha is the first storm to form in the Bay of Bengal this year and is expected to hit several coastal areas in Bangladesh on 14 May with wind speeds of up to 175 km/h.After made its landfall in the coast between Cox’s Bazar (Bangladesh) and Kyaukphyu (Myanmar) near Sittwe (Myanmar). At most, Catastrophic Damage-causing winds was possible especially in the areas of Rakhine State and Chin State, and Severe Damage-causing winds is possible in the ...
disaster responseearth observationenvironmentalgeospatialsatellite imagerysustainabilitysynthetic aperture radar
The Sentinel1 Single Look Complex (SLC) unzipped dataset contains Synthetic Aperture Radar (SAR) data from the European Space Agency’s Sentinel-1 mission. Different from the zipped data provided by ESA, this dataset allows direct access to individual swaths required for a given study area, thus drastically minimizing the storage and downloading time requirements of a project. Since the data is stored on S3, users can utilize the boto3 library and s3 get_object method to read the entire content of the object into the memory for processing, without actually having to download it. The Sentinel-1 ...
astronomyimagingsatellite imagerysurvey
Spitzer was an infrared astronomy space telescope with imaging from 3 to 160 microns and spectroscopy from 5 to 37 microns, launched into an Earth-trailing solar orbit as the last of NASA's Great Observatories. The SEIP Super Mosaics include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. Data from multiple programs are combined where appropriate. Cryogenic Release v3.0 includes Spitzer data taken during commissioning and cryogenic operations, including calibration data.
climateearth observationearthquakessatellite imageryweather
Various kinds of weather raw data and charts from Central Weather Administration.
climateearth observationearthquakessatellite imageryweather
Various kinds of weather raw data and charts from Central Weather Bureau.
cogcomputer visiondeep learningearth observationfloodsgeospatialmachine learningsatellite imagerysynthetic aperture radar
This dataset consists of chips of Sentinel-1 and Sentinel-2 satellite data. Each Sentinel-1 chip contains a corresponding label for water and each Sentinel-2 chip contains a corresponding label for water and clouds. Data is stored in folders by a unique event identifier as the folder name. Within each event folder there are subfolders for Sentinel-1 (s1) and Sentinel-2 (s2) data. Each chip is contained in its own sub-folder with the folder name being the source image id, followed by a unique chip identifier consisting of a hyphenated set of 5 numbers. All bands of the satellite data, as well a...
earth observationenvironmentalgeospatialsatellite imagery
ISS SERVIR Environmental Research and Visualization System (ISERV) was a fully-automated prototype camera aboard the International Space Station that was tasked to capture high-resolution Earth imagery of specific locations at 3-7 frames per second. In the course of its regular operations during 2013 and 2014, ISERV's camera acquired images that can be used primaliry in use is environmental and disaster management.
satellite imageryspace weather
The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite 19 (GOES-19) is the fourth and final satellite in the
Geostationary Operational Environmental Satellites (GOES) – R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental monitoring
system.
The GOES-R Series provides advanced imagery and atmospheric measurements, real-time mapping of lightning activity, and space weather observations. As a part of the
Space Weather Follow On (SWFO) Mission, the GOES-19 spacecraft contains a Compact Coronagraph-1 (...
agriculturedisaster responseearth observationgeospatialsatellite imagerystacsustainabilitysynthetic aperture radar
The official Government of Canada (GC) 🍁 Near Real-time (NRT) Sentinel Mirror connected to the EU Copernicus programme, focused on Canadian coverage. In 2015, Canada joined the Sentinel collaborative ground segment which introduced an NRT Sentinel mirror site for users and programs inside the Government of Canada (GC). In 2022, the Commission signed a Copernicus Arrangement with the Canadian Space Agency with the aim to share each other’s satellite Earth Observation data on the basis of reciprocity. Further to this arrangement as well as ongoing Open Government efforts, the private mirror was made open to the public, here on the AWS Open Dataset Registry.
Le Sentinel Mirror officiel du gouvernement du Canada (GC) 🍁 en temps quasi réel (NRT) connecté au [programme Copernicus de l'UE] (https://www.copernicus.eu), axé sur la couverture canadienne. En 2015, le Canada a rejoint le segment terrestre collaboratif Sentinel qui a introduit un site miroir NRT Sentinel pour les utilisateurs et les programmes au sein du gouvernement du Canada (GC). . En 2022, la Commission a signé un accord Copernicus avec l'Agence spatiale canadienne dans le but de partager mutuellement les données satellitaires d'observation de la Terre sur la base de la réciprocité. Suite à cet arrangement ainsi qu'aux efforts continus de gouvernement ouvert, le m...
earth observationgeospatialimage processingsatellite imagerystacsynthetic aperture radar
Umbra satellites generate the highest resolution Synthetic Aperture Radar (SAR) imagery ever offered from space, up to 16-cm resolution. SAR can capture images at night, through cloud cover, smoke and rain. SAR is unique in its abilities to monitor changes. The Open Data Program (ODP) features over twenty diverse time-series locations that are updated frequently, allowing users to experiment with SAR's capabilities. We offer single-looked spotlight mode in either 16cm, 25cm, 35cm, 50cm, or 1m resolution, and multi-looked spotlight mode. The ODP also features an assorted collection of over ...
activity detectionagriculturecogdisaster responseearth observationenvironmentalgeospatialimage processingland covernatural resourcesatellite imagerystac
The Venµs science mission is a joint research mission undertaken by CNES and ISA, the Israel Space Agency. It aims to demonstrate the effectiveness of high-resolution multi-temporal observation optimised through Copernicus, the global environmental and security monitoring programme. Venµs was launched from the Centre Spatial Guyanais by a VEGA rocket, during the night from 2017, August 1st to 2nd. Thanks to its multispectral camera (12 spectral bands in the visible and near-infrared ranges, with spectral characteristics provided here), it acquires imagery every 1-2 days over 100+ areas at...
satellite imagery
This dataset combines SSL4EO-S12 and SSL4EO-L to create a multi-view dataset for multi-modal fusion using self-supervised learning for earth observation.
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water.There are multiple retrieval algorithms for estimating Chl-a. These data use the Carder method implemented in the SeaDAS processing software l2gen and described in Carder K. L., Chen F. R., Lee Z. P., Hawes S. K. and Cannizzaro J. P. (2003), MODIS Ocean Science Team Algorithm Theoretical Basis Docume...
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the Garver-Siegel-Maritorena (GSM) method implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (Details →
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the OC3 method recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OC3 algorithm is described at http://oceancolor.gsfc.nasa.gov/cms/atbd/chlor_a (and links th...
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the OCI method (Hu et al 2012, doi: 10.1029/2011jc007395) recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OCI algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/ (and links therei...
biologyoceanssatellite imagery
The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the Garver-Siegel-Maritorena (GSM) method implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (Details →
biologyoceanssatellite imagery
The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the OC3 method recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OC3 algorithm is described at http://oceancolor.gsfc.nasa.gov/cms/atbd/chlor_a (and links ...
biologyoceanssatellite imagery
The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the OCI method (Hu et al 2012, doi: 10.1029/2011jc007395) recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OCI algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/ (and links there...
biologyoceanssatellite imagery
The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the Garver-Siegel-Maritorena (GSM) method implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (Details →
biologyoceanssatellite imagery
The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the OC3 method recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OC3 algorithm is described at http://oceancolor.gsfc.nasa.gov/cms/atbd/chlor_a (and links th...
biologyoceanssatellite imagery
The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. There are multiple retrieval algorithms for estimating Chl-a. These data use the OCI method (Hu et al 2012, doi: 10.1029/2011jc007395) recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OCI algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/ (and links therein...
oceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the diffuse attenuation coefficient (Kd) at 490nm wavelength which provides information on how light is attenuated in the water column. It is defined as the scaling length of the exponential decrease of the downwelling irradiance and has units (m^-1). The MODIS K490 product estimates Kd at 490nm wavelength, using a semi-empirical model based on the ratio of water leaving radiances at 490nm and 555nm....
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. An empirical relationship is then used to compute an estimate of the relative abundance of three phytoplankton size classes (micro, nano and picoplankton). The methods used to decompose chl_oc3 are described by Brewin et al in two papers in 2010 and 2012. The two methods, denoted Brewin2010at and Br...
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. Modelling is then used to compute an estimate of the Net Primary Productivity (NPP).The model used is based on the standard vertically generalised production model (VGPM). The VGPM is a "chlorophyll-based" model that estimates net primary production from chlorophyll using a temperature-de...
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. Modelling is then used to compute an estimate of the Net Primary Productivity (NPP).The model used is based on the standard vertically generalised production model (VGPM). The VGPM is a "chlorophyll-based" model that estimates net primary production from chlorophyll using a temperature-de...
oceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These measurements at discrete wavelengths represent the spectrum of light leaving the water surface, and the shape of the spectrum is characteristic of the water optical properties.Moore et al. (2009) applied a clustering technique to spectra to identify 8 sets of discrete optical water types. This product "owt_csiro" is produced using a CSIRO implementation of the Moore et al. algorithm, and testing shows that it closely reproduces the res...
biologyoceanssatellite imagery
The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. An empirical relationship is then used to compute an estimate of the relative abundance of three phytoplankton size classes (micro, nano and picoplankton). The methods used to decompose chl_oc3 are described by Brewin et al in two papers in 2010 and 2012. The two methods, denoted Brewin2010at and Br...
biologyoceanssatellite imagery
The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the diffuse attenuation coefficient (Kd) at 490nm wavelength which provides information on how light is attenuated in the water column. It is defined as the scaling length of the exponential decrease of the downwelling irradiance, and has units (m^-1). The VIIRS K490 product estimates Kd at 490nm wavelength, using a semi-empirical model based on the ratio of water leaving radiances at 490nm and 555...
biologyoceanssatellite imagery
The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the diffuse attenuation coefficient (Kd) at 490nm wavelength which provides information on how light is attenuated in the water column. It is defined as the scaling length of the exponential decrease of the downwelling irradiance, and has units (m^-1). The VIIRS K490 product estimates Kd at 490nm wavelength, using a semi-empirical model based on the ratio of water leaving radiances at 490nm and 555nm...
oceanssatellite imagery
This is a multi-sensor SSTfnd L3S product for a single 24 hour period, derived using sea surface temperature retrievals from the VIIRS sensor on the Suomi-NPP satellite and JPSS series of satellites, and AVHRR sensor on the NOAA and Metop series of Polar-orbiting satellites. The sensors and satellite platforms contributing to each file are listed in the sensor and platform global attributes in the file header. The SSTfnd is derived by adding a constant 0.17 degC to the NOAA AVHRR SSTskin observations, and 0 degC to the Metop and VIIRS SSTsubskin observations, after rejecting observations with ...
oceanssatellite imagery
This is a multi-sensor SSTfnd L3S product for a single 72 hour period, derived using sea surface temperature retrievals from the VIIRS sensor on the Suomi-NPP satellite and JPSS series of satellites, and AVHRR sensor on the NOAA and Metop series of Polar-orbiting satellites. The sensors and satellite platforms contributing to each file are listed in the sensor and platform global attributes in the file header. The SSTfnd is derived by adding a constant 0.17 degC to the NOAA AVHRR SSTskin observations, and 0 degC to the Metop and VIIRS SSTsubskin observations, after rejecting observations with ...
oceanssatellite imagery
This is a single-sensor multi-satellite SSTfnd product for a single 24 hour period, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites. It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70°E to 170°W, 20°N to 70°S. Each grid cell contains the 24 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. The SSTfnd is derived by adding a constant 0.17 degC to the SSTskin observations after rejecting observations with low surface wind speeds (<6m/s by day and <2m/s at night) (see http://www.bom.gov.au/amoj/docs/2011/beggs.pdf). Matchups with buoy SSTfnd observations indicate typical 2014 biases of < 0.01 degC and standard deviations of 0.6 degC. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html for further informatio...
oceanssatellite imagery
This is a single-sensor SSTfnd product for a single 24 hour period, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites. It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the Southern Ocean region 3°E to 158°W, 27°S to 78°S. Each grid cell contains the 24 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. The SSTfnd is derived by adding a constant 0.17 degC to the SSTskin observations after rejecting observations with low surface wind speeds (<6m/s by day and <2m/s at night) (see http://www.bom.gov.au/amoj/docs/2011/beggs.pdf). Matchups with buoy SSTfnd observations indicate typical 2014 biases of < 0.01 degC and standard deviations of 0.6 degC. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at Details →
oceanssatellite imagery
This is a single-sensor multi-satellite SSTskin product for 1 month of consecutive day-time periods, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites. It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70°E to 170°W, 20°N to 70°S. Each grid cell contains the 1 month average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. Matchups with buoy SST observations for the central date (adjusted to skin depths) indicate typical 2014 biases of < 0.3 degC and standard deviations of 0.7 degC. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html for further inform...
oceanssatellite imagery
This is a single-sensor multi-satellite SSTfnd product for a 144 hour period, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites. It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70°E to 170°W, 20°N to 70°S. Each grid cell contains the 144 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. The SSTfnd is derived by adding a constant 0.17 degC to the SSTskin observations after rejecting observations with low surface wind speeds (<6m/s by day and <2m/s at night) (see http://www.bom.gov.au/amoj/docs/2011/beggs.pdf). Matchups with buoy SSTfnd observations for the central date indicate typical 2014 biases of < 0.03 degC and standard deviations of 0.6 degC. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html f...
oceanssatellite imagery
This is a regional GHRSST level 3 collated (L3C) dataset on 0.02-degree rectangular grid over the Australasian domain (70E to 190E, 70S to 20N) based on retrievals from the AHI imager on board Himawari-8 satellite. The Bureau of Meteorology (Bureau) produces Integrated Marine Observing System (IMOS) satellite SST products in the International Group for High Resolution SST (GHRSST) GDS2 file formats for Himawari-8 in real time and delayed mode. This product is composed of reprocessed multi-swath SSTskin retrievals obtained from compositing IMOS Himawari-8 hourly L3C files over the night (before dawn).
Every 10 minutes, the Himawari-8 full disk is processed to retrieve SSTs by using the Radiative Transfer Model (RTTOV12.3) and Bayesian cloud clearing method based on the ESA CCI SST code developed at the University of Reading. The hourly product on native grid is then produced by compositing over multiple swaths for the previous 1 hour. Further, L3C-01day SST fields on native grid are obtained by selecting the highest quality data from the hourly SST records with priority being given to the value the closest in time to the product nominal hour. The L3C-1day night product on native grid is th...
oceanssatellite imagery
An International Group for High-Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends infra-red SST observations from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA and METOP polar-orbiting satellites, microwave SST observations from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on GCOM-W, and in situ data from ships, and dr...
oceanssatellite imagery
An International Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends infra-red SST observations from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA and METOP polar-orbiting satellites, microwave SST observations from the Advanced Microwave Scanning Radiometer-2 (...
astronomyimagingobject detectionsatellite imagerysurvey
Euclid launched in July 2023 as a European Space Agency (ESA) mission with involvement by NASA. The primary science goals of Euclid are to better understand the composition and evolution of the dark Universe. The Euclid mission will provide space-based imaging and spectroscopy as well as supporting ground-based imaging to achieve these primary goals. These data will be archived by multiple global repositories, including IRSA, where they will support transformational work in many areas of astrophysics. Euclid Quick Release 1 (Q1) consists of ~30 TB of imaging, spectroscopy, and catalogs coverin...