NASA Prediction of Worldwide Energy Resources (POWER)

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...

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Usage examples

• The Contribution of Solar Brightening to the US Maize Yield Trend by Tollenaar, T., J. Fridgen, P. Tyagi, P. W. Stackhouse Jr., and S. Kumudini
• NASA POWER NetCDF Data Download by The POWER Project
• Global Surface Solar Energy Anomalies Including El Nino and La Nina Years. by C. H., and Coauthors
• POWER Data Access Viewer (DAV) by The POWER Project
• Association between solar insolation and a history of suicide attempts in bipolar I disorder by Bauer M, et al., Stackhouse PW Jr., et al.

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MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree

agricultureair temperatureatmospherebiodiversityclimatecoastaldatacenterecosystemsglobalhydrologyicelandmetadatanetcdfoceansopendapwater

M2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: Details →

Usage examples

• Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances by Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts
• A catchment-based approach to modeling land surface processes in a GCM, Part 1, Model Structure by Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar
• Assessment of MERRA-2 Land Surface Hydrology Estimates. by Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. De Lannoy
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• How to Read and Plot NetCDF MERRA-2 Data in Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio 0.625 x 0.5 degree

agricultureair qualityatmospherebiodiversitycarbonclimatecoastaldatacenterecosystemsglobalhydrologyicelandmetadatanetcdfopendapwater

M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: Details →

Usage examples

• Three-dimensional variational analysis with spatially inhomogeneous covariances by Wu, W.-S., R.J. Purser and D.F. Parrish
• Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances by Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts
• Design and implementation of components in the Earth System Modeling Framework by Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva
• Land Surface Precipitation in MERRA-2. by Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka
• Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. by Molod, A., L. Takacs, M. Suarez, and J. Bacmeister

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MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields

agricultureair temperatureatmospherebiodiversityclimatecoastaldatacenterecosystemsglobalhydrologyicelandmetadatanetcdfopendapwater

M2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data field is available every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: Details →

Usage examples

• Design and implementation of components in the Earth System Modeling Framework by Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva
• The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). by Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al.
• Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances by Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. by Molod, A., L. Takacs, M. Suarez, and J. Bacmeister

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MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree

agricultureair temperatureatmospherebiodiversityclimatecoastaldatacenterecosystemsglobalhydrologyicelandmetadatanetcdfopendapwater

M2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: Details →

Usage examples

• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shre
• Land Surface Precipitation in MERRA-2. by Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka
• Data assimilation using incremental analysis updates by Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina
• Design and implementation of components in the Earth System Modeling Framework by Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva
• The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation. by Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and C.J. Flynn

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Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST)

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...

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Usage examples

• THREDDS server by PO.DAAC
• Python Reader Software by PO.DAAC
• Improving our knowledge about the oceans by providing cloud-based access to large datasets by Chelle Gentemann
• GHRSST Data Processing Specification by GHRSST Project
• OPeNDAP server by Physical Oceanography Distributed Active Archive Center (PO.DAAC)

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Catalina Sky Survey (CSS) subset data on AWS

astronomyobject detectionplanetarysurvey

Raw data that discovers Near Earth Objects (NEOs) which potentially could impact Earth

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Usage examples

• Transform Tool by Planetary Data System (PDS) Engineering Node
• Catalina Sky Survey Telescopes and Instrumentation by R. Seaman, E. Christensen, A. Gibbs, S. Larson, F. Shelly
• History of the Catalina Sky Survey by S. Larson, edited by R. Seaman
• Astropy by The Astropy Developers
• PDS4 Viewer by Small Bodies Node of the NASA Planetary Data System group at the University of Maryland.

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NASA Earth Exchange (NEX) Data Collection

climateCMIP5natural resourcesustainability

A collection of downscaled climate change projections, derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth Exchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and LOCA (CMIP5).

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Usage examples

• Climate Downscaling Using YNet: a Deep Convolutional Network with Skip Connections and Fusion by Yumin Liu, Auroop Ganguly, and Jennifer Dy
• Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321. by Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013)
• Explainable deep learning for insights in El Nino and river flows by Yumin Liu, Kate Duffy, Jennifer G. Dy, and Auroop R. Ganguly
• Potential changes in cooling degree day under different global warming levels and shared socioeconomic pathways in West Africa by Oluwarotimi Delano Thierry Odou, Heidi Heinrichs Ursula, Rabani Adamou, Thierry Godjo, and Mounkaila S Moussa
• Sample Python code to analyze NASA-NEX data. by Jiawei Zhuang

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AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC

atmosphereclimatedatacenterearth observationglobalhdfmetadataopendaporbit

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm....

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Usage examples

• Updates to the absolute radiometric accuracy of the AIRS on Aqua by Pagano, T.S., Aumann, H.H., Broberg, S., Manning, E., Overoye, K., and Weiler, M.
• AIRS Level-1C and applications to cross-calibration with MODIS and CrIS by Evan M. Manning, Hartmut H. Aumann, and Ali Behrangi
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Radiometric Stability Validation of 17 Years of AIRS Data Using Sea Surface Temperatures. by Aumann, H.H. Brogerg, S.,Manning, E., and Pagaino, T.
• Validation of the Atmospheric Infrared Sounder radiative transfer algorithm by Strow, L.L, Hannon, S.E, De-Souza Machado, S., Motteler, H.E., and Tobin, D.C.

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Solar Dynamics Observatory (SDO) Machine Learning Dataset

machine learningNASA SMD AI

The v1 dataset includes AIA/HMI observations 2010-2018 and v2 includes AIA/HMI observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.

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Usage examples

• ML applications based on the SDOMLv2 dataset by Wright, Paul J.
• ML applications based on the SDOMLv1 dataset by Salvatelli, Valentina; dos Santos, Luiz F. G.; Bose, Souvik; Neuberg, Brad; Cheung, Mark C. M.; Janvier, Miho; Jin, Meng; Gal, Yarin; Boerner, Paul; Baydin, Atılım Güneş
• Scripts for generating the SDOMLv2 dataset by Jin, Meng
• Scripts for generating the SDOMLv1 dataset by Fouhey, David F.; Jin, Meng
• SDOMLv2 Github by Wright, Paul J.; Jin, Meng; Cheung, Mark C. M.

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GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE) at GES DISC

atmosphereclimatedatacenterforecastglobalhdfhydrologylandmetadataopendapradarwater

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiome...

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Usage examples

• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
• How to Read IMERG Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak

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GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 (GPM_3IMERGM) at GES DISC

atmosphereclimatedatacenterforecastglobalhdfhydrologylandmetadataopendapradarwater

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiome...

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Usage examples

• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
• How to Read IMERG Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider

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GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH) at GES DISC

atmosphereclimatedatacenterforecastglobalhdfhydrologylandmetadataopendapradarwater

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiome...

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Usage examples

• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• How to Read IMERG Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL) at GES DISC

atmosphereclimatedatacenterforecastglobalhdfhydrologylandmetadataopendapradarwater

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar...

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Usage examples

• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• How to Read IMERG Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak

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NASA / USGS Lunar Orbiter Laser Altimeter Cloud Optimized Point Cloud

elevationlidarplanetarystac

The lunar orbiter laser altimeter (LOLA) has collected and released almost 7 billion individual laser altimeter returns from the lunar surface. This dataset includes individual altimetry returns scraped from the Planetary Data System (PDS) LOLA Reduced Data Record (RDR) Query Tool, V2.0. Data are organized in 15˚ x 15˚ (longitude/latitude) sections, compressed and encoded into the Cloud Optimized Point Cloud (COPC) file format, and collected into a Spatio-Temporal Asset Catalog (STAC) collection for query and analysis. The data are in latitude, longitude, and radius (X, Y, Z) format with the p...

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Usage examples

• Discovering and Downloading Data with Python by J. Laura
• Discovering and Downloading Data via the Command Line by J. Laura
• Point Data Abstraction Library (PDAL) by PDAL Contributors
• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• PySTAC Client by PySTAC-Client Contributors

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OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1)

cogdatacenterearth observationicelandland covermetadatasurface waterwater

This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.

The digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal res
...

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Usage examples

• Stream and Viz DSWx-HLS via Direct HTTPS by M. Grace Bato
• Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests by Jones, John W
• Getting Started with OPERA DSWx Product by K. Devlin and M. Grace Bato
• Working with OPERA Dynamic Surface Water Extent (DSWx) Data by Nicholas Tarpinian
• Access DSWx-HLS S3 by M. Grace Bato

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Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density

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.

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Usage examples

• Medium Post: COG Talk — Part 1: What’s new? by Vincent Sarago
• COG Viewer by Development Seed
• COG Application Programming Interface (API) by Development Seed
• Rasterio (Python Library) for access to geospatial raster data by Mapbox
• COG metadata example  by Development Seed

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GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC

atmosphereclimatecoastaldatacenterglobalhydrologylandmetadatanetcdfopendap

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG Early Daily 10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gaug...

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Usage examples

• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider

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GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC

climatecoastaldatacenterglobalhydrologyicelandmetadatanetcdfopendap

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG Final Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared and rain gauge dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. The latency of the derived Final Daily product depends on the delivery of the IMERG Final Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2. The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = S...

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Usage examples

• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC

atmosphereclimatecoastaldatacenterglobalhydrologylandmetadatanetcdfopendap

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for ...

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Usage examples

• Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
• Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
• Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Digital Terrain Models

cogelevationplanetarystac

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 are digital terrain models derived using the NASA A...

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Usage examples

• Discovering and Downloading Data with Python by J. Laura
• PySTAC Client by PySTAC-Client Contributors
• Discovering and Downloading Data via the Command Line by J. Laura
• Querying for Data in an ROI and Loading it into QGIS by J. Laura

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JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations

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...

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Usage examples

• Discovering and Downloading Data via the Command Line by J. Laura
• PySTAC Client by PySTAC-Client Contributors
• Discovering and Downloading Data with Python by J. Laura
• Querying for Data in an ROI and Loading it into QGIS by J. Laura

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NASA / USGS Controlled Europa DTMs

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...

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Usage examples

• Discovering and Downloading Data via the Command Line by J. Laura
• Discovering and Downloading Data with Python by J. Laura
• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• PySTAC Client by PySTAC-Client Contributors

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NASA / USGS Controlled THEMIS Mosaics

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...

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Usage examples

• PySTAC Client by PySTAC-Client Contributors
• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• Discovering and Downloading Data with Python by J. Laura
• Discovering and Downloading Data via the Command Line by J. Laura

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NASA / USGS Europa Controlled Observation Mosaics

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
...

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Usage examples

• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• Discovering and Downloading Data via the Command Line by J. Laura
• PySTAC Client by PySTAC-Client Contributors
• Discovering and Downloading Data with Python by J. Laura

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NASA / USGS Europa Controlled Observations

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
...

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Usage examples

• Discovering and Downloading Data via the Command Line by J. Laura
• Discovering and Downloading Data with Python by J. Laura
• PySTAC Client by PySTAC-Client Contributors
• Querying for Data in an ROI and Loading it into QGIS by J. Laura

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NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs

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...

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Usage examples

• Discovering and Downloading Data via the Command Line by J. Laura
• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• Discovering and Downloading Data with Python by J. Laura
• PySTAC Client by PySTAC-Client Contributors

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NASA / USGS Released HiRISE Digital Terrain Models

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 ...

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Usage examples

• Discovering and Downloading Data via the Command Line by J. Laura
• PySTAC Client by PySTAC-Client Contributors
• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• Discovering and Downloading Data with Python by J. Laura

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NASA / USGS Uncontrolled HiRISE RDRs

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...

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Usage examples

• Discovering and Downloading Data with Python by J. Laura
• Discovering and Downloading Data via the Command Line by J. Laura
• Querying for Data in an ROI and Loading it into QGIS by J. Laura
• PySTAC Client by PySTAC-Client Contributors

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OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)

coastalearth observationgeoscienceglobalhdficelandmetadataoceansorbitradarsentinel-1soil moisturesynthetic aperture radartiffxml

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data with a near global scope and temporal sampling coincident with the availability of S1...

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Usage examples

• Thermal Denoising of Products Generated by the S-1 IPF by Riccardo Piantanida
• An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding by Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley
• RTC Landslide Example by K. Venkataramani
• Load, Mosaic, and Visualize OPERA RTC-S1 Data by K. Venkataramani

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GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)

datacenterearth observationglobalicemetadataoceansparquetuswater

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave S...

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Usage examples

• A multi-scale high-resolution analysis of global sea surface temperature by Chin, T.M, J. Vazquez-Cuervo, and E.M. Armstrong
• MUR Sea Surface Temperature Analysis of Washington State by Zoë Walschots
• Using Sea Surface Temperature and Sea Surface Height Data for Hurricane Helene by Julie Sanchez

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NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)

air temperatureclimateclimate modelclimate projectionsCMIP6cogearth observationenvironmentalglobalmodelNASA Center for Climate Simulation (NCCS)near-surface relative humiditynear-surface specific humiditynetcdfprecipitation

The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed...

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Usage examples

• NASA and ASDI announce no-cost access to important climate dataset on the AWS Cloud by Dr. Manil Maskey and Ana Pinheiro Privette
• NEX-GDDP-CMIP6 Dashboard by NASA
• NASA Global Daily Downscaled Projections, CMIP6 by Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T. and Nemani, R.

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NASA High Energy Astrophysics Mission Data

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...

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Usage examples

• HEASoft by NASA/HEASARC
• HEASARC Cloud access page by NASA/HEASARC
• HEASARC Bibliography by Various; curated by HEASARC

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NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA)

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 ...

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Usage examples

• LAMBDA cloud access by NASA/LAMBDA
• LAMBDA Tools by NASA/LAMBDA
• CMB Experiment Bibliography by Various; curated by LAMBDA

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NASA SOHO/LASCO2 comet challenge on AWS

astronomymachine learningNASA SMD AI

The SOHO/LASCO data set (prepared for the challenge hosted in Topcoder) provided here comes from the instrument’s C2 telescope and comprises approximately 36,000 images spread across 2,950 comet observations. The human eye is a very sensitive tool and it is the only tool currently used to reliably detect new comets in SOHO data - particularly comets that are very faint and embedded in the instrument background noise. Bright comets can be easily detected in the LASCO data by relatively simple automated algorithms, but the majority of comets observed by the instrument are extremely faint, noise-...

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Usage examples

• Winners Selected for the NASA SOHO Comet Search with Artificial Intelligence Open-Science Challenge by Denise Hill
• Topcoder Challenge Finds Two New Comets For NASA by Annika Nagy
• Topcoder NASA Comet Discovery: A Recap by TopCoder

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NASA Space Biology Open Science Data Repository (OSDR)

bioinformaticsbiologyGeneLabgenomicimaginglife sciencesspace biology

NASA’s Space Biology Open Science Data Repository (OSDR) introduces a one-stop site where users can explore and contribute a variety of NASA open science biological data. This site consolidates data from the Ames Life Sciences Data Archive (ALSDA) and GeneLab and includes information about the broader NASA Open Science and Open Data initiatives, all at one centralized location. Our mission is to maximize the utilization of the valuable biological research resources and enable new discoveries.

OSDR introduces access to data generated from spaceflight and space relevant experiments that explore
...

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Usage examples

• Advancing the Integration of Biosciences Data Sharing to Further Enable Space Exploration by Ryan T. Scott, Kirill Grigorev, Graham Mackintosh, Samrawit G. Gebre, Christopher E. Mason, Martha E. Del Alto, Sylvain V. Costes
• GeneLab: Omics database for spaceflight experiments by Shayoni Ray, Samrawit Gebre, Homer Fogle, Daniel C Berrios, Peter B Tran, Jonathan M Galazka, Sylvain V Costes
• NASA GeneLab: interfaces for the exploration of space omics data by Daniel C Berrios, Jonathan Galazka, Kirill Grigorev, Samrawit Gebre, Sylvain V Costes

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OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1)

cogearth observationenvironmentalgloballandland coverland use

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the DIST-ALERT data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIS...

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Usage examples

• Visualization and Exploration of OPERA DIST-ANN-HLS Product Layers by C. Speed and M. Grace Bato
• Visualizing and Analyzing the OPERA DIST-ANN-HLS Product to Explore Land-Use Change in Brazil by C. Speed and M. Grace Bato
• Visualizing and Analyzing the OPERA DIST-ANN-HLS Product to Visualize Wildfire Impact in Northern Quebec by C. Speed and M. Grace Bato

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OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)

coastalcogearth observationgeoscienceglobalicelandmetadataoceansorbitradarsentinel-1synthetic aperture radartiffxml

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and th...

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Usage examples

• Load, Mosaic, and Visualize OPERA RTC-S1 Data by K. Venkataramani
• An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding by Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley
• RTC Landslide Example by K. Venkataramani

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Astrophysics Division Galaxy Segmentation Benchmark Dataset

astronomymachine learningNASA SMD AIsegmentation

Pan-STARSS imaging data and associated labels for galaxy segmentation into galactic centers, galactic bars, spiral arms and foreground stars derived from citizen scientist labels from the Galaxy Zoo: 3D project.

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Usage examples

• Pan-STARRS Pixel Processing: Detrending, Warping, Stacking by C. Z. Waters, E. A. Magnier, P. A. Price, K. C. Chambers, W. S. Burgett, P. W. Draper, H. A. Flewelling, K. W. Hodapp, M. E. Huber, R. Jedicke, N. Kaiser, R.-P. Kudritzki, R. H. Lupton, N. Metcalfe, A. Rest, W. E. Sweeney, J. L. Tonry, R. J. Wainscoat, and W. M. Wood-Vase
• Galaxy Zoo: 3D – crowdsourced bar, spiral, and foreground star masks for MaNGA target galaxies by Karen L Masters, Coleman Krawczyk, Shoaib Shamsi, Alexander Todd, Daniel Finnegan, Matthew Bershady, Kevin Bundy, Brian Cherinka, Amelia Fraser-McKelvie, Dhanesh Krishnarao, Sandor Kruk, Richard R Lane, David Law, Chris Lintott, Michael Merrifield, Brooke Simmons, Anne-Marie Weijmans, Renbin Yan

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Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset

fluorescence imagingGeneLabgeneticgenetic mapslife sciencesmicroscopyNASA SMD AI

Fluorescence microscopy images of individual nuclei from mouse fibroblast cells, irradiated with Fe particles or X-rays with fluorescent foci indicating 53BP1 positivity, a marker of DNA damage. These are maximum intensity projections of 9-layer microscopy Z-stacks.

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Usage examples

• Dose, LET and Strain Dependence of Radiation-Induced 53BP1 Foci in 15 Mouse Strains Ex Vivo Introducing Novel DNA Damage Metrics by Sébastien Penninckx, Egle Cekanaviciute, Charlotte Degorre, Elodie Guiet, Louise Viger, Stéphane Lucasb, Sylvain V. Costes
• NASA SMD AI Workshop Report by SMD Artificial Intelligence (AI) Initiative

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Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset

gene expressionGeneLabgeneticgenetic mapslife sciencesNASA SMD AIspace biology

RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network.

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Usage examples

• NASA SMD AI Workshop Report by SMD Artificial Intelligence (AI) Initiative
• Adversarial generation of gene expression data by Ramon Viñas, Helena Andrés-Terré, Pietro Liò, Kevin Bryson

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GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002

biodiversitycarbondatacenterearth observationenergyglobalhdficelandland coverlidarmetadataorbiturbanwater

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.Known Issues

• Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
• Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers.
• Known Issues: Section 8 of the User Guide provides additional information on known issues.

• Metadata has been updated to include spatial coordinates.
• Granule size has been reduced from one full ISS orbit (5.83 GB) to four segments per orbit (1.48 GB).
• Filename has been updated to include segment number and version number.
• Improved geolocation for an orbital segment.
• Added elevation from the SRTM digital elevation model for comparison.
• Modified the method to predict an optimum algorithm setting group per laser shot.
• Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.
• Added selected_mode_flag dataset to root beam group using selected algorithm.
• Removed shots when the laser is not firing.
• Modified file name to include segment number and dataset version. Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Getting Started with GEDI L2A Version 2 Data in Python by Land Processes Distributed Active Archive Center (LP DAAC)
  • How to: Find and Access GEDI Data by Land Processes Distributed Active Archive Center (LP DAAC)

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GHRSST Level 2P Global Sea Surface Skin Temperature from the MODIS on the NASA Terra satellite (GDS2)

atmospheredatacenterearth observationgloballandmarinemetadatanetcdfoceansorbit

NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the l...

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Usage examples

• A decade of sea surface temperature from MODIS by Kilpatrick, K.A., Podestá, G., Walsh, S., Williams, E., Halliwell, V., Szczodrak, M., Brown, O.B., Minnett, P.J., & Evans, R.
• Direct S3 Access tutorial by PODAAC

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GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) at GES DISC

atmospherecontaminationdatacenterearth observationglobalhdfmetadataopendapradarwater

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. .2ADPR provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products: those derived from the Ku-band frequency over a wide swath (245 km), those derived from the Ka-band frequency over a narrow swath (125 km), and those derived from the dual-frequency data over the narrow swath. The Ka-ban...

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Usage examples

• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
• How to Read IMERG Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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NASA Physical Sciences Informatics (PSI)

chemistryfluid dynamicsmaterials sciencephysicsspace biology

NASA's Physical Sciences Research Program, along with its predecessors, has conducted significant fundamental and applied research in the physical sciences. The International Space Station (ISS) is an orbiting laboratory that provides an ideal facility to conduct long-duration experiments in the near absence of gravity and allows continuous and interactive research similar to Earth-based laboratories. This enables scientists to pursue innovations and discoveries not currently achievable by other means. NASA's Physical Sciences Research Program also benefits from collaborations with several of the ISS international partners—Europe, Russia, Japan, and Canada—and foreign governments with space programs, such as France, Germany and Italy.

In fulfillment of the Open Science model, NASA's Physical Sciences Research Program is pleased to offer the PSI data repository for physical science experiments performed in reduced-gravity environments such as the ISS, Space Shuttle flights, and Free-flyers. PSI also includes data from some related ground-based studies. The PSI system is accessible and open to the public. This provides the opportunity for researchers to data mine results from prior flight investigations, expanding on the research performed. This approach will allow numerous ground-based investigations to be conducted fro
...

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Usage examples

• Researcher's Guide to Physical Sciences Informatics System by NASA ISS Program Science Office
• PSI System Demonstration by Cheryl Payne

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Nighttime-Fire-Flare

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.

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Usage examples

• Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA's Black Marble product suite by Srija Chakraborty, Tomohiro Oda, Virginia Kalb, Zhuosen Wang, Miguel O Román
• NASA SMD AI Workshop Report by SMD Artificial Intelligence Machine Learning (AI/ML) Working Group

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OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1)

coastalearth observationhdficelandmetadataoceansorbitradarsentinel-1synthetic aperture radarxml

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-S1 product, as they are produced only once ...

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Usage examples

• Generate inteferograms without the need to download OPERA CSLC-S1 products locally by M. Grace Bato and K. Devlin
• Generate interferograms and map the lava flow emplacement using OPERA CSLC-S1 by M. Grace Bato

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OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)

coastalearth observationhdficelandmetadataoceansorbitradarsentinel-1synthetic aperture radarxml

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model a...

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Usage examples

• Generate interferograms and map the lava flow emplacement using OPERA CSLC-S1 by M. Grace Bato
• Generate inteferograms without the need to download OPERA CSLC-S1 products locally by M. Grace Bato and K. Devlin

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OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1)

cogdatacenterearth observationgloballandorbitradarsentinel-1surface waterwater

This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the...

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Usage examples

• Working with OPERA Dynamic Surface Water Extent (DSWx) Data by Nicholas Tarpinian
• Generate Flood Maps without downloading OPERA DSWx-S1 products locally by S. Sangha

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OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1)

cogearth observationenvironmentalgloballandland coverland usesatellite imagery

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance in...

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Usage examples

• Getting Started with OPERA DIST Product by M. Grace Bato and R. Dhillon
• Getting Started with OPERA DIST-ALERT-HLS Products by R. Dhillon and M. Grace Bato

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OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0)

cogearth observationenvironmentalgloballandland coverland use

The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the OPERA_L3_DIST-ALERT-HLS V1 data product which was released on March 14, 2024, and has achieved stage 1 validation.The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrum...

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Usage examples

• Getting Started with OPERA DIST-ALERT-HLS Products by R. Dhillon and M. Grace Bato
• Getting Started with OPERA DIST Product by M. Grace Bato and R. Dhillon

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Terra Fusion Data Sampler

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...

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Usage examples

• TerraFusion GitHub by University of Illinois
• Basic Terra fusion product algorithm theoretical basis and data specifications by Zhao, Guangu; Yang, Muqun; Clipp, Landon; Gao, Yizhao; Lee, Joe H.

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Zwicky Transient Facility (ZTF)

astronomyobject detectionparquetsurvey

The Zwicky Transient Facility (ZTF) is a time-domain astronomy survey that uses the Palomar 48 inch Schmidt telescope and a custom-built wide-field camera to image the night sky in three photometric filters (g, r, and i). It is a fully-automated survey aimed at a systematic exploration of optical transient phenomena. It completes a scan of the observable northern sky approximately every three nights.

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Usage examples

• Multi-Wavelength Light Curves Tutorial by Fornax Initiative
• IRSA Notebook Tutorials by Caltech/IPAC-IRSA

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3-Band Cryo Data | Wide-field Infrared Survey Explorer (WISE)

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.

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Usage examples

• Notebook Tutorials by Caltech/IPAC-IRSA

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ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020

atmospherecogcogearth observationgloballandradar

This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which ...

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Usage examples

• Accessing Data through ORNL DAAC Web Services by ORNL DAAC

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AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC

atmospheredatacenterearth observationglobalhdficelandmetadataopendaporbit

WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Max...

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Usage examples

• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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ASTER Level 1T Precision Terrain Corrected Registered At-Sensor Radiance V004

cogcogearth observationgloballandorbit

The Terra 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 bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands.The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover).For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution as both a text file and single band browse images with the valid GCPs overlaid.This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding AST_L1A dataset.Known Issues

• Since October 1, 2017, a correction addresses zero-filled scans in low-latitude, ascending orbit (nighttime) TIR data. Additional details are available in the ASTER L1T User Advisory.
• Data from the SWIR bands collected after April 2008 may show anomalous saturation and striping. See the ASTER SWIR User Advisory for further information.

• Enhanced Geolocation Accuracy: Version 4 uses Collection 2 Ground Control Points (GCPs) compared against Global Land Survey (GLS) 2000 standards to improve positional accuracy.
• Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by Tsuchida and others (2020), published in Remote Sensing. Read our doc on how to get AWS Credentials to...

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  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006

atmospheredatacenterearth observationglobalhdficelandwater

This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. Read our doc on how to get AWS Creden...

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Usage examples

• Accessing and working with ICESat-2 data in the cloud by Andy Barrett, Jennifer Roebuck, Amy Steiker

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ATLAS/ICESat-2 L3A Land and Vegetation Height V006

atmospheredatacenterearth observationglobalhdficeland

This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. Read our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME

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Usage examples

• Accessing and working with ICESat-2 data in the cloud by Andy Barrett, Jennifer Roebuck, and Amy Steiker.

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All-Sky Data | Wide-field Infrared Survey Explorer (WISE)

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.

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Usage examples

• Notebook Tutorials by Caltech/IPAC-IRSA

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AllWISE Data | Wide-field Infrared Survey Explorer (WISE)

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.

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Usage examples

• Notebook Tutorials by Caltech/IPAC-IRSA

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Astrophysics Division Galaxy Morphology Benchmark Dataset

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.

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• Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging by Kyle W. Willett, Melanie A. Galloway, Steven P. Bamford, Chris J. Lintott, Karen L. Masters, Claudia Scarlata, B. D. Simmons, Melanie Beck, Carolin N. Cardamone, Edmond Cheung, Edward M. Edmondson, Lucy F. Fortson, Roger L. Griffith, Boris Häußler, Anna Han, Ross Hart, Thomas Melvin, Michael Parrish, Kevin Schawinski, R. J. Smethurst, Arfon M. Smith

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GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1

earth observationecosystemsglobalhdflandland coverlidaropendap

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global ...

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• Searching and Downloading GEDI L4A Dataset by Rupesh Shrestha

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HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0

atmospherecogdatacenterearth observationgeospatialglobalicelandmetadataorbitsatellite imagerystacsurface watertileswaterxml

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.Known Issues

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Usage examples

• Getting Started with Cloud-Native HLS Data in Python by Mahsa Jami, Erik A. Bolch, Cole K. Krehbiel, Aaron M. Friesz, Brianna M. Lind

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HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0

cogdatacenterearth observationgeospatialglobalhdficelandmetadataorbitsatellite imagerystacsurface watertileswaterxml

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.Known Issues

...

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• Getting Started with Cloud-Native HLS Data in Python by Mahsa Jami, Erik A. Bolch, Cole K. Krehbiel, Aaron M. Friesz, Brianna M. Lind

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Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree V2 (GPM_IMERG_LandSeaMask) at GES DISC

atmospherecoastaldatacentergloballandmetadatanetcdfopendap

Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.This land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions.Users sho...

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• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061

datacenterearth observationgeospatialglobalhdficelandopendapsatellite imagery

The MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. Known Issues

• Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website.
• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

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MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061

datacenterearth observationgeospatialglobalhdficelandopendap

The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). Known Issues

• Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website.
• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m

atmospheredatacenterearth observationenvironmentalglobalhdfmetadataopendaporbit

The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of:

1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution
2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands
3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.
4. Calibration data for all channels (scale and offset)
5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ .

Usage examples

• MODIS Level 1B - Calibrated Radiances - Natural Color RGB - 500m by EUMETSAT

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MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061

atmosphereearth observationevapotranspirationgeospatialglobalhdflandland coveropendapwater

The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.Known Issues

• Operational and uncertainty issues are provided under Section 3 in the User Guide.
• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-u...

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MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061

earth observationgeospatialglobalhdflandopendapsatellite imagery

The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061

datacenterearth observationgeospatialglobalhdficelandopendapsatellite imagery

The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products. Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061

datacenterearth observationgeospatialglobalhdficelandopendap

The MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MOD09GA). Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061

datacenterearth observationgeospatialglobalhdficelandopendapsatellite imagery

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061

earth observationgeospatialglobalhdflandopendapsatellite imagerytiles

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.The MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.Known Issues

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MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061

earth observationgeospatialglobalhdflandopendaptiles

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo (MCD43A3) and Nadir BRDF-Adjusted Reflectance (NBAR) (MCD43A4) products.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.The MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product. Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m V061

earth observationgeospatialglobalhdflandopendapsatellite imagerytiles

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: Details →

  Usage examples

  • Download Files from S3 Using boto3 by LPDAAC

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Mars Spectrometry 2: Gas Chromatography for the Sample Analysis at Mars Data (SAM) Instrument

analyticsarchivesdeep learningmachine learningNASA SMD AIplanetary

NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.

Because communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover.

This project aimed at building a model to automatically analyze gas chromatography mass spectrometry (GCMS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars.

More information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm.

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Usage examples

• Mars Spectrometry: Mars Spectrometry 2 (Challenge Results) by DrivenData team and NASA partners

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Mars Spectrometry: Detect Evidence for Past Habitability

analyticsarchivesdeep learningmachine learningNASA SMD AIplanetary

NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.

Because communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover.

This project aimed at building a model to automatically analyze evolved gas analysis mass spectrometry (EGA-MS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars.

More information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm.

We request that you ci...

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Usage examples

• Mars Spectrometry: Detect Evidence for Past Habitability (Challenge Results) by DrivenData team and NASA partners

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NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) at GES DISC

atmosphereclimatedatacenterforecastglobalmetadatanetcdfopendap

These data originate from NOAA/NCEP.The NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. The original record started from February, 2000, but in June, 2025 it was extended back to January, 1998.The leading edge of data availability is delayed by about 24 hours from real-time to abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT data providers).The data contain globally-merged (60°S-60°N) 4-km pixel-resolution IR brightness temperature data (equivalent blackbody temps), merged from the European, Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16/17/18/19, METEOSAT-5/7/8/9/10/11, and GMS-5/MTSat-1R/2/Himawari-8/9).The global geo-IR are dynamically calibrated to GOES East, using a 35 day trailing inter-calibration using time/space-matched IR Tb’s at the mid-point between sub-satellite positions. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken. The data have been corrected for "zenith angle dependence", in which IR temperatures for locations far from satellite nadir are erroneously cold due to a combination of geometric effects and radiometric path extinction effects (Joyce et al. 2001). Finally, the data are re-navigated for parallax, which shifts the geo-location of the GEO-IR footprints to approximately account for the cloud tops that the IR “sees” being displaced away from their actual geographic location when viewed along a slanted path. These corrections allow for the merging of the IR data from the various GEO-satellites with greatly reduced discontinuities at GEO-satellite data boundaries. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken.The NASA GES DISC is curating these data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad range of applications to access the data directly, without the need to cope with the original binary data format. In addition to the direct download of netCDF-4 data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats using the OPeNDAP interface.

Similarities with the original

Differences from the original

1. The data in the netCDF-4 files are already converted to real (float) values of Brightness Temperatures in Kelvin. There is no need to further scale these data. The netCDF-4 format is machine-independent and users need not worry about the endian-ness of their machines.
2. To meet the requirements of collection spatial metadata, the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). It is also starting from -60 (South).

Acknowledgements

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Usage examples

• How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.

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NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE)

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.

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• Notebook Tutorials by Caltech/IPAC-IRSA

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NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE)

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.

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• Notebook Tutorials by Caltech/IPAC-IRSA

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OPERA Surface Displacement from Sentinel-1 validated product (Version 1)

earth observationlandmetadatanetcdforbitradarsentinel-1synthetic aperture radarxmlzarr

The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides. The OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR input...

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• Inspect DISP-S1 Layers by M. Grace Bato

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OpenUniverse 2024 Simulated Roman & Rubin Images

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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.

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• Notebook Tutorials by Caltech/IPAC-IRSA

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SENTINEL-1A_DUAL_POL_GRD_HIGH_RES

agriculturecoastalearth observationearthquakesecosystemsicelandland coverland usemetadataoceansradarsentinel-1stacsurface watersynthetic aperture radartiffurbanwater

Sentinel-1A Dual-pol ground projected high and full resolution images Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

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• Interferometric Synthetic Aperture Radar Tutorial by LiveEO

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SENTINEL-1A_SLC

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Sentinel-1A slant-range product Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

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• Interferometric Synthetic Aperture Radar Tutorial by LiveEO

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SENTINEL-1B_DUAL_POL_GRD_HIGH_RES

agriculturecoastalearthquakesecosystemsicelandland coverland usemetadataoceansradarsentinel-1stacsurface watersynthetic aperture radartiffurbanwater

Sentinel-1B Dual-pol ground projected high and full resolution images Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

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• Interferometric Synthetic Aperture Radar Tutorial by LiveEO

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SENTINEL-1B_SLC

agriculturecoastalearthquakesecosystemsicelandland coverland usemetadataoceansorbitradarsentinel-1stacsurface watersynthetic aperture radartiffurbanwater

Sentinel-1B slant-range product Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

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• Interferometric Synthetic Aperture Radar Tutorial by LiveEO

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SPHEREx Quick Release (QR): An All-Sky Spectral Survey

astronomyimagingobject detectionsatellite imagerysurvey

The Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is a NASA Astrophysics Medium-class Explorer (MIDEX) mission launched in March 2025. During its planned two-year mission, SPHEREx will perform the first ever all-sky spectral survey in the optical to near-infrared (0.75-5 microns). SPHEREx Quick Release (QR) is the first data release. SPHEREx data will be used to probe inflation and the early universe, trace the history of galactic light production, and investigate the origin of planetary systems and biogenic ices, in addition to contribu...

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• Notebook Tutorials by Caltech/IPAC-IRSA

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Spitzer Enhanced Imaging Products (SEIP) Super Mosaics

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.

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• Notebook Tutorials by Caltech/IPAC-IRSA

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Unblurred Coadds of the Wide-field Infrared Survey Explorer (unWISE)

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unWISE is a reprocessing of Wide-field Infrared Survey Explorer (WISE) data which preserves the native angular resolution and is optimized for forced photometry. WISE was a NASA satellite producing all-sky imaging in four infrared bands centered at 3.4, 4.6, 12 and 22 microns (W1, W2, W3, and W4) starting in 2010 until the coolant was exhausted in 2011. It was reactivated in 2013 as NEOWISE and continued imaging in W1 and W2 until 2024.

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• Notebook Tutorials by Caltech/IPAC-IRSA

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MISR Level 1B2 Ellipsoid Data V004

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MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing.MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The...

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NASA SOTERIA Simulation Testbed Data

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Commercial pilot simulation data during safety-of-flight scenarios.

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• Python Processing Code by Tyler Fettrow
• SOTERIA Simulation - Experimental Methods, Data Processing, and Data Quality by Tyler Fettrow, Chad Stephens, Lance Prinzel, Jon Holbrook, Sepher Bastami, Michael Stewart, Kathryn Ballard, Daniel Kiggins

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Euclid Quick Release 1 (Q1)

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...

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• Notebook Tutorials by Caltech/IPAC-IRSA

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