Changes in the land surface can impact climate, terrestrial ecosystems, and hydrology. Land surface-related data, including land cover type, land surface temperature, and topography, are critical for monitoring agricultural practices and water resource availability and for guiding interventions when necessary.
Land surface reflectance is a measure of the fraction of incoming solar radiation reflected from Earth's surface to a satellite-borne or aircraft-borne sensor. These data are useful because they provide an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption, which is referred to as atmospheric correction.
Land surface reflectance data can be used for visualizing the surface as well as for computing metrics and creating models that are useful for specific analysis. Agricultural production estimates must be restricted to crop-specific areas (e.g., corn, soybeans, etc.) to avoid confusion with other crops, natural vegetation, and areas of no vegetation. This allows specific crops to be observed over time using sustained land imaging and multi-spectral high-resolution imagery.
Commonly Used Land Surface Reflectance Data at a Glance
An asterisk (*) next to an entry indicating that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) within three hours from satellite observation. Imagery is generally available 3-5 hours after observation. While not intended for scientific research, NRT data are good resources for monitoring ongoing or time-critical events.
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ |
Observation or Model |
File Format |
---|---|---|---|---|---|---|---|
15 m, 30 m | Global | Variable | 2000-present | Terra | ASTER | Observation | HDF-EOS, GeoTIFF |
500 m, 1 km, 0.05° | Global | 1-2 days | 2000-present | Terra, Aqua | *MODIS | Observation | HDF-EOS5 |
500 m, 1 km, 5,600 m |
Global | 1-2 days |
2017-present |
Suomi NPP | *VIIRS | Observation | HDF5, HDF-EOS5 |
15 m, 30 m, 60 m |
Global | 16 days | 1982-present (various missions) | Landsat 4, 5, 7, 8, 9 |
OLI-2, |
Observation |
GeoTIFF |
30 m | Near-Global (no Antarctic) | 2-3 days | 2013-present | HLS (Landsat 8, 9 + Sentinel-2A/B) | OLI, OLI-2, MSI | Observation | Cloud Optimized GeoTIFF (COG) |
Use the Data
- Use Cases and Articles
-
- A Rough Year for Rice in California: The ongoing drought has cut rice acreage in the Sacramento Valley in half
- Data Chat: Dr. Jeffrey Masek: The Harmonized Landsat Sentinel-2 (HLS) project offers daily, 30-meter global land surface data products to facilitate a wide range of terrestrial Earth science research
- A Harmonious New Dataset: The provisional public release of the Harmonized Landsat Sentinel-2 (HLS) dataset through NASA’s LP DAAC opens new avenues for global terrestrial research
- Tutorials
-
- Getting Started with VIIRS Surface Reflectance Data: All about Accessing the Data
- Getting Started with VIIRS Surface Reflectance Data: Using the Data
- Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Accessing the Data
- Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Using the Data
- Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Interpreting Quality Information
- Advancing Science Capabilities with Data Harmonization: NASA's Harmonized Landsat Sentinel-2 Product
- GIS-Ready Tools, Tutorials, and Data
- Data Access Tools
-
- EarthExplorer connects users with USGS satellite, aircraft, and other remote sensing inventories through interactive and textual-based query capabilities.
- Sentinel Hub EO Browser provides a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place.
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- Choosing a Data Access Tool: AppEEARS
- Using AppEEARS Quality Service to Extract Information from MODIS Quality Layers
- Choosing a Data Access Tool: AppEEARS Area Sampler
- MODIS/VIIRS Subsetting Tools Suite:
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- For more information on a suite of NASA data tools for searching and ordering, data handling, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting. LP DAAC also offers an E-Learning section with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS. In addition to the user interface, AppEEARS also provides a publicly accessible API
- Getting Started with Cloud-Native Harmonized Landsat Sentinel-2 (HLS) Data in R
- Jupyter Notebook Tutorial for getting started with HLS data in Python
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
- Working with AppEEARS NetCDF-4 Output Data in Python
- Masking, Visualizing, and Plotting AppEEARS Output GeoTIFF Time Series in Python
- Working with AppEEARS NetCDF-4 Output Data in R
Earth Observation Data by Sensor
ASTER
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument is a cooperative effort between NASA and Japan's Ministry of Economy, Trade and Industry (METI).
ASTER Surface Reflectance data can be visualized and interactively explored using NASA Worldview:
Research quality ASTER data products are available through Earthdata Search:
ETM, OLI, OLI-2, TIRS-2
The Enhanced Thematic Mapper (ETM+), the Operational Land Imager (OLI) and OLI-2, and the Thermal Infrared Sensor-2 (TIRS-2) are aboard the joint NASA/USGS Landsat series of satellites.
OLI data can be visualized and interactively explored using NASA Worldview:
- Landsat 8 and 9 OLI/OLI-2 in NASA Worldview (base layers shown)
- Landsat 8 and 9 OLI/OLI-2 in NASA Worldview (base layers turned off)
Research quality Landsat land surface reflectance data products can be accessed directly using USGS EarthExplorer:
- Landsat 7 ETM+
- Landsat 8 OLI
- Landsat 9 OLI-2
HLS
Harmonized Landsat Sentinel-2 (HLS) data provide consistent global observation of Earth’s surface reflectance and top-of-atmosphere (TOA) brightness data from the Landsat OLI and OLI-2 and the ESA (European Space Agency) Multi-Spectral Instrument (MSI) aboard the Sentinel-1A/B satellites every 2-3 days with 30 meter spatial resolution.
HLS Surface Reflectance data can be visualized and interactively explored using NASA Worldview:
Research quality HLS data products can be accessed directly from Earthdata Search:
The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) tool offers a simple and efficient way to access, transform, and visualize geospatial data from a variety of federal data archives, including USGS Landsat Analysis Ready Data (ARD) surface reflectance products.
MODIS
Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance products provide an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption.
MODIS Surface Reflectance data can be visualized and interactively explored using NASA Worldview:
Multiple Geographic Information Systems (GIS) MODIS Surface Reflectance data layers with different band combinations are available through Esri’s ArcGIS OnLine (AGOL). NASA GIS data may be used with open-source GIS software such as Quantum GIS or Geographic Resources Analysis Support System (GRASS). Learn more about these tools in the Use the Data section below.
Research quality MODIS data products can be accessed directly from Earthdata Search:
Near real-time (NRT) MODIS Surface Reflectance data are available through NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) within 60 to 125 minutes after a satellite observation:
VIIRS
The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites.
VIIRS Surface Reflectance data can be visualized and interactively explored using NASA Worldview:
VIIRS/NPP Land Surface Reflectance Data from Earthdata Search
Data are available daily and 8-day at various spatial resolutions.
Near real-time (NRT) VIIRS Surface Reflectance data are available through LANCE within 60 to 125 minutes after a satellite observation:
- VIIRS NRT Data in LANCE for 357m, 750m
- VIIRS NRD Data in Lance for 1km and 500
Land Surface Temperature (LST) describes processes such as the exchange of energy and water between the land surface and Earth's atmosphere. LST influences the rate and timing of plant growth and is affected by the albedo, or reflectance, of a surface. These data can improve decision-making for water use and irrigation strategies, and are also an indicator for crop health and water stress.
Commonly Used Land Surface Temperature Data at a Glance
An asterisk (*) next to an entry indicating that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) within three hours from satellite observation. Imagery is generally available 3-5 hours after observation. While not intended for scientific research, NRT data are good resources for monitoring ongoing or time-critical events.
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
1 km, 0.05° |
Global | 1-2 days | 2000-present | Terra, Aqua | *MODIS | Observation | HDF-EOS |
750 km, 1 km | Global | Daily, Multi-day, Monthly | 2012-present | Suomi NPP | *VIIRS | Observation | HDF-EOS5 |
0.25°, 0.5°, 1° |
Global | Monthly | 2002-present | Aqua, Suomi NPP | MODIS, VIIRS | Model | HDF-EOS5 |
30 m | Global | 16 days | 2013-present | Landsat 8, 9 | OLI, OLI-2 | Observation | GeoTIFF |
15 m | Global | Varies | 2000-present | Terra | ASTER | Observation | HDF-EOS |
~70 m | Global | Varies | 2018-present | International Space Station | ECOSTRESS | Observation |
GeoTIFF |
0.01°, 0.1°, 0.125°, 0.25° |
Global | Hourly, 3-hourly, Daily, Monthly | 1948-present | N/A | LDAS | Model | netCDF |
0.5° x 0.625° | Global | Monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Tutorials
-
- Data Access and Visualization of Model Data at the NASA GES DISC
- Getting Started with MODIS Land Surface Temperature Data (Part 1): Accessing the Data
- Getting Started with MODIS Land Surface Temperature Data (Part 2): Using the Data
- Getting Started with MODIS Land Surface Temperature Data (Part 3): Interpreting Quality Information
- Exploring Earth's Land Surface with Suomi NPP NASA VIIRS Land Data
- Discover and Access Landsat Analysis Ready Data (ARD) from the USGS Archive
- GIS-Ready Tools, Tutorials, and Data
-
- How to Import MERRA Surface Product Data into ArcGIS
- Accessing ORNL DAAC OGC services in popular GIS tools
- Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
- Data Access Tools
-
- Landsat Data Visualization Tools:
- Sentinel Hub EO Browser provides a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place.
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- Programming Tools
-
- Webinar: R you Ready to Python? An Introduction to Working with Land Remote Sensing Data in R and Python
- Webinar: NASA ORNL DAAC MODIS and VIIRS Data Tools and Services at your Fingertips
- Webinar: Navigating NASA's LP DAAC to Find Answers to your Deepest Land Data Questions
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
Earth Observation Data by Sensor
MODIS
MODIS LST data can be visualized and interactively explored using NASA Worldview:
MODIS LST data can be visualized in Giovanni:
Research quality LST data products can be accessed directly from Earthdata Search and also are available through the Data Pool at NASA’s Land Processes DAAC (LP DAAC).
The AppEEARS tool and MODIS subsetting tools can be used to quickly extract a subset of MODIS data for a region of interest.
- Terra MODIS LST
- Aqua MODIS LST
- For both of the MODIS products above, select daily, 8-day, or monthly at 1 km or 0.05 degree resolution
- Terra MODIS LST/3-Band Emissivity
- Aqua MODIS LST/3-Band Emissivity
- For both of the MODIS products above, select 5-min, daily, and 8-day at 1 km resolution
Near real-time (NRT) MODIS LST data are available through LANCE within 60 to 125 minutes after a satellite observation.
ASTER
Research quality LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are available in HDF-EOS format:
- ASTER Surface Kinetic Temperature
- ASTER surface temperature products are processed on-demand and must be requested with additional parameters. Note that there is a limit to 2,000 granules per order.
MODIS/ASTER
A suite of MODIS LST and Emissivity (LST&E) products are available that combine MODIS data with ASTER data to leverage the strengths from both sensors. These integrated LST data can be visualized and interactively explored using NASA Worldview:
ECOSTRESS
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress.
The AppEEARS tool and MODIS subsetting tools can be used to quickly extract a subset of ECOSTRESS data for a region of interest.
VIIRS
The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites.
Research quality LST data products from VIIRS:
- VIIRS LST/3-Band Emissivity data from Earthdata Search
- Choose daily or 8-day at 1 km resolution
Near real-time (NRT) VIRS LST data are available through LANCE within 60 to 125 minutes after a satellite observation:
OLI, OLI-2
LST data are produced as part of the NASA/USGS Landsat series of Earth observing missions.
LST data through the USGS EarthExplorer
- Landsat 8 OLI: April 2013 to present
- Landsat 9 OLI-2: February 2022 to present
Model Data
MODIS/VIIRS
The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night.
LDAS
NASA’s Land Data Assimilation System (LDAS) uses sophisticated numerical models of physical processes to integrate multiple satellite and ground-based data products. LDAS uses advanced land surface modeling and assimilation techniques to deliver physically consistent and spatially and temporally continuous data.
LDAS and its various projects have a variety of uses:
- Water resources applications
- Drought and wetness monitoring
- Numerical weather prediction studies
- Interpretation of satellite and ground-based observations
LDAS components and projects:
- North American Land Data Assimilation System (NLDAS)
- Global Land Data Assimilation System (GLDAS)
- Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS)
- Western Land Data Assimilation System (WLDAS)
LDAS LST data can be visualized in Giovanni. Monthly data are available from 2002:
- FLDAS Surface Temperature data in Giovanni
- GLDAS Surface Temperature data in Giovanni
- NLDAS Surface Temperature data in Giovanni
Research quality NLDAS LST data products:
Please see the LDAS FAQ for additional ways to obtain, subset, and view LDAS datasets, including fast time series "data rods", file format conversion, and other web services that provide LDAS data.
MERRA-2
Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is the latest version of global atmospheric satellite data reanalysis produced by NASA’s Global Modeling and Assimilation Office (GMAO). The dataset covers 1980-present with the latency of approximately three weeks after the end of a month.
NRT imagery of LST data can be interactively explored using NASA Worldview:
MERRA-2 data can be visualized in Giovanni. Monthly data are available starting in 2002:
MERRA-2 LST data are available for download in Earthdata Search.
An understanding of topography is essential when assessing an area's runoff potential, the availability of water in lower-lying areas, a site’s suitability for planting, and other site-specific applications. These data are often in the form of Digital Elevation Models (DEMs) or grids with values representing the height of a cell.
Commonly Used Topography/Elevation Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name |
Observation or Model | File Format |
---|---|---|---|---|---|---|---|
30 m for U.S., 60 m, 90 m, 1 km for global | Global | One-time estimate | 2000 | SRTM | N/A | Observation | HGT, NetCDF4 |
30 m | Global | Multi-year | 2000-2013 | Terra | ASTER | Observation |
HDF-EOS or GeoTIFF |
25 m diameter |
51.6° N and 51.6° S |
One-time estimate | 2019-2022 | International Space Station | GEDI | Observation and Model | HDF5 |
Use the Data
- Use Cases and Articles
-
- Bhutan Agriculture
- Lasering in on Corn Fields: New research uses 3D lidar profiles of Earth's surface to map where corn is being grown.
- NASA Harvest Partners At Stanford Expand Lidar Applications To Create Wall-To-Wall Crop Type Mapping
- New in CSDA: High Resolution Digital Elevation Models
- Earth in the Third Dimension: First GEDI Data Available
- Tutorials
- GIS-Ready Tools, Tutorials, and Data
-
- Working with Land Remote Sensing Data in a GIS Environment
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- NASA GIS Tutorials and How-Tos
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- Data Access Tools
-
- EarthExplorer connects users with USGS satellite, aircraft, and other remote sensing inventories through interactive and textual-based query capabilities.
- Sentinel Hub EO Browser provides a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place.
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- GEDI Subsetting with Earthdata Search Tutorial
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
Earth Observation Data by Sensor
SRTM
One of the most common topography data sources is the Shuttle Radar Topography Mission (SRTM). SRTM provides a DEM of all land between 60° north and 56° south latitude, which encompasses about 80% of Earth's landmass.
ASTER
The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator).
DEM data accuracy is typically very sensitive to vegetation cover; however, data from the ASTER instrument tend to perform better over specific landcover types. Applications include identifying crop stress, mapping surface temperatures of soils and geology, and measuring surface heat balance.
The NASADEM data product was released in February 2020 and provides 1 arc-second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products.
Imagery can be interactively explored using NASA Worldview:
Research quality topography data products are available from Earthdata Search:
- SRTM from Earthdata Search
- These data were acquired in 2000 and are in raw format (with the ".hgt" file extension), and can be opened in most Geographic Information Systems (GIS), such as ArcGIS or QGIS; data are also customizable to GeoTIFF
- ASTER Digital Elevation Model V003
- NASADEM from Earthdata Search
In addition to Earthdata Search, SRTM and ASTER data can be accessed through AppEEARS.
GEDI
The Global Ecosystem Dynamics Investigation (GEDI) Level 3 Land Surface Metrics dataset provides gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation, and counts of laser footprints per 1 km x 1 km grid cells within 52° north and south latitude. Data are available from April 2019 through 2022. Level 3 gridded products can be used to create digital elevation models, characterize important carbon and water cycling processes, and more.
Users may download customized subsets (Level 3 and Level 4) of GEDI data using the Spatial Data Access Tool through the at The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC).
GEDI L3 Gridded Land Surface Metrics data can be visualized and interactively explored using NASA Worldview:
Research quality data can be accessed using Earthdata Search:
Vegetation is a key component of the overall Earth system. It plays a critical role in the movement of water at all levels, including the ecosystem and landscape levels. Assessments of vegetation health, including vegetation greenness, land cover type, evapotranspiration, and evaporative stress, are critical to monitoring agricultural practices and water resource availability and for decising when interventions are necessary.
Aboveground Biomass (AGB) is a widely-used variable that helps characterize the growth of crops and forecast their yields.
Commonly Used Aboveground Biomass Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
25 m diameter, 1 km grid | 52° N to 52° S latitude |
One-time estimate | 2019-2022 | International Space Station | GEDI | Observation and Model | GeoTIFF |
Use the Data
- Use Cases and Articles
-
- Biomass Earthdata Dashboard: The Biomass Harmonization Activity
- New, Gridded Level 3 Data Product Facilitates the Use of GEDI Mission Data
- Earth in the Third Dimension: First GEDI Data Available
- ORNL DAAC Releases GEDI Level 4B Dataset Offering Gridded Estimates of Aboveground Biomass Density
- Mapping Carbon Beyond Forests: New Harmonized Global Maps of Above and Belowground Biomass Carbon
- Tutorials
- Data Visualization
- GIS-Ready Tools, Tutorials, and Data
-
- Spatial Data Access Tool (SDAT) Geospatial Data Visualization/Download based on OGC Standards
- Accessing ORNL DAAC OGC services in popular GIS tools
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service. Web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- ORNL DAAC Subsetting Tools
- For more information on a suite of NASA data tools for searching and ordering, data handling, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- Tutorials on GEDI Science Data Products (GEDI L3 and L4)
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
- NASA Earthdata Application Programming Interfaces (APIs)
Earth Observation Data by Sensor
GEDI
The Global Ecosystem Dynamics Investigation (GEDI) is a joint mission between NASA and the University of Maryland, with the instrument installed on the International Space Station. Data acquired using the instrument’s three lasers are used to construct detailed 3D maps of forest canopy height and the distribution of branches and leaves. GEDI data play an important role in understanding the amounts of biomass and carbon forests store and how much they lose when disturbed. GEDI data also can be used to study plant and animal habitats and biodiversity along with how these change over time. New research supported by NASA Harvest reveals these data also can be used to map where different types of crops are being grown.
Research quality Level 3 data can be accessed using Earthdata Search:
Model Data
GEDI
GEDI Level 4B data provide gridded 1km x 1km estimates of mean aboveground biomass density (AGBD). This dataset provides gridded estimates of aboveground biomass density at a greater accuracy and resolution than previously available.
Model GEDI AGBD data can be accessed using Earthdata Search:
The Evaporative Stress Index (ESI) describes temporal anomalies in ET and highlights areas with anomalously high or low rates of water use across the land surface. ESI also demonstrates the capability for capturing early signals of "flash drought" brought on by extended periods of hot, dry, and windy conditions that can lead to rapid soil moisture depletion.
Commonly Used Evaporative Stress Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
30 m, 70 m |
CONUS |
Target areas every 1-7 days |
2018-present | International Space Station | ECOSTRESS | Model | GeoTIFF |
500 m | Global | Multi-day | 2001-present | Terra | MODIS | Model | HDF-EOS |
Use the Data
- Use Cases and Articles
-
- NASA DEVELOP Project - Arizona Agriculture Spring 2017: Demonstrating the Potential Applications of ECOSTRESS Evapotranspiration Products in Plant Phenotyping and Predicting Patterns in Global Species Richness
- NASA DEVELOP Project - Costa Rica Agriculture II Fall 2016: Analyzing Advantages of ECOSTRESS Data as a Tool for Drought Detection and Water Management Practices
- NASA DEVELOP Project - Costa Rica Agriculture Summer 2016: Utilizing simulated ECOSTRESS data products to estimate the changes in water stress in crops over a daily cycle and to evaluate the utility of future ECOSTRESS data streams for supporting agricultural water resources management
- Assessing Drought-Induced Vegetation Stress and its Impact on Crop Production Across Iowa
- Tutorials
-
- Webinar: ECOSTRESS: NASA's Next-Generation Mission to Measure Evapotranspiration from the International Space Station
- ARSET - New Sensor Highlight: ECOSTRESS
- NASA ARSET: Evapotranspiration & Evaporative Stress Index for Agricultural Applications, Part 4/4
- Hands-on Workshop for Accessing, Processing, and Analyzing ECOSTRESS Data
- GIS-Ready Tools, Tutorials, and Data
-
- Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- Visualizing ECOSTRESS in QGIS
- Users may access observed and modeled data, as well as visualize those data directly through ClimateSERV. Datasets include Evaporative Stress Index, SMAP Soil Moisture, Rainfall, IMERG, and NDVI
- To visualize MODIS ESI data in a web map service, see ArcGIS REST Service: 4-Week ESI, 12-Week ESI
- Accessing ORNL DAAC OGC services in popular GIS tools
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- Data Access Tools
-
- Users may also access data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, SMAP Soil Moisture, Rainfall, and NDVI
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats.
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- ECOSTRESS in AppEEARS - LP.DAAC tool to quickly subset and reproject ECOSTRESS data
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- Webinar: ECOSTRESS: NASA's Next-Generation Mission to Measure Evapotranspiration from the International Space Station
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS. In addition to the user interface, AppEEARS also provides a publicly accessible API
- Jupyter Notebook: Convert from ECOSTRESS swath to geotiff
- Jupyter Notebook: Apply the L2 cloud mask to L2 Land Surface Temperature AppEEARS output
- Jupyter Notebook: Convert from ECOSTRESS swath to geotiff
- NASA Earthdata Application Programming Interfaces (APIs)
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
Model Data
ECOSTRESS
The Ecostress ESI product is derived from the ratio of Level 3 actual ET to potential ET (PET). ESI data can be used to assess agricultural drought and observe vegetation stress.
Water use efficiency (WUE) is the ratio of carbon stored by plants to water evaporated by plants. This ratio is given as grams of carbon stored per kilogram of water evaporated over the course of the day from sunrise to sunset on the day when the ECOSTRESS data granule is acquired.
Level 4 (modeled) ECOSTRESS ESI and WUE products can be accessed using Earthdata Search:
- ECOSTRESS ESI in Earthdata Search
- ECOSTRESS Evaporative Stress Index dis-ALEXI Daily L4 from LPDAAC
- ECOSTRESS Evaporative Stress Index dis-ALEXI USDA Daily L4 Global 30 m
- ECOSTRESS WUE in Earthdata Search
ClimateSERV
Users may access and visualize observed and modeled data directly through ClimateSERV. ClimateSERV was created by SERVIR, a joint initiative of NASA, USAID, and geospatial organizations in Asia, Africa, and Latin America. ClimateSERV allows users to visualize and download historical rainfall data, vegetation condition data, and 180-day forecasts of rainfall and temperature. Datasets include ESI, SMAP Soil Moisture, Rainfall, IMERG, and NDVI.
MODIS
MODIS ESI data can be visualized in a web map service via ArcGIS REST Service:
Evapotranspiration (ET) is the sum of evaporation from the land surface and transpiration in vegetation. ET measurements are useful in monitoring and assessing water availability, drought conditions, and crop production. An increase in available energy through changes in cloud cover, seasonal lengthening of daylight, and similar variables favors primary production and ET. This, in turn, extracts available water from the soil and represents the largest component of consumptive water use in the U.S. If this soil water is not replenished through rain or irrigation, plants close their stomata to retain water and primary production is reduced. By comparing observed ET to a modeled expectation of crop water requirements, ET observations can be used to schedule irrigation applications and improve agricultural water management.
ET can't be measured directly with satellite instruments because it is modeled based on variables including land surface temperature, air temperature, and solar radiation. NASA has Level 4 data products that incorporate daily meteorological reanalysis data with remote sensing data that provide estimations of ET, such as the MODIS MOD16 product.
Commonly Used Evapotranspiration Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
30 m, 70 m |
Global/CONUS | About 1-7 days | 2018-present | International Space Station | ECOSTRESS | Observation | GeoTIFF |
500 m | Global | 1-2 days | 2000-present | Terra | MODIS | Observation and Model | HDF5 |
500 m | Global | 1-2 days | 2002-present | Aqua | MODIS | Observation and Model | HDF-EOS |
30 m | Global | 16 days | 1982-present | Landsat 4, 5, 7, 8, 9 | TM, ETM+, OLI/TIRS | Observation | GeoTIFF |
30 m | Near-global (no Antarctic) | 2-3 days | 2013-present | HLS |
OLI, OLI-2, MSI |
Observation | Cloud Optimized GeoTIFF (COG) |
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 1984-present | N/A | LDAS | Model | netCDF |
Use the Data
- Use Cases and Articles
-
- Developing an Evapotranspiration Climatology to Analyze Spatiotemporal Water Budget Patterns for Agriculture and Natural Resources Managers in the Midwest
- Determining Crop Coefficients Using Remote Sensing for the Maipo River Valley Basin in Chile
- Transforming Water Management in the U.S. West with NASA Data
- Landsat Imagery Sheds Light on Agricultural Water Use
- Evapotranspiration: Watching Over Water Use
- Farming Food for All
- Tutorials
-
- ARSET - Satellite Remote Sensing for Agricultural Applications
- ARSET - Applications of Remote Sensing-Based Evapotranspiration Data Products for Agricultural and Water Resource Management
- ARSET - Applications of Remote Sensing to Soil Moisture and Evapotranspiration
- ARSET - Water Resource Management Using NASA Earth Science Data
- Data Access and Visualization of Model Data at the NASA GES DISC
- ECOSTRESS: NASA's Next-Generation Mission to Measure Evapotranspiration from the ISS
- Hands-on Workshop for Accessing, Processing, and Analyzing ECOSTRESS Data
- Remote Sensing with LP DAAC Data Assets and Cloud Processing – Part 1: Introduction to NASA's Remote Sensing Datasets and Tools
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Getting to Know Land Data from NASA’s LP DAAC
- Accessing ORNL DAAC OGC services in popular GIS tools
- Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- OpenET provides ET data from multiple satellite-driven models, and also calculates a single “ensemble value” from those models. Landsat is currently the primary satellite dataset used within the OpenET platform. Multiple models implemented within the OpenET framework also integrate data from GOES, Sentinel-2, Suomi NPP, Terra, Aqua and other satellites to produce ET data at a range of spatial and temporal scales
- Introduction to OpenET
- Data Rods Explorer (DRE) enables users to browse several NASA-hosted datasets. The interface enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable. The key variables are precipitation, wind, temperature, surface downward radiation flux, heat flux, humidity, soil moisture, groundwater, runoff, and evapotranspiration
- Data Customizing Tools
-
- Soil Moisture Visualizer incorporates in-situ, airborne, and remote sensing data into one easy-to-use platform. This integration helps to validate and calibrate the data and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- ECOSTRESS Swath to Grid Conversion Script
- Workshop for Accessing, Processing, and Analyzing ECOSTRESS Data
- Working with ECOSTRESS Evapotranspiration Data
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS. In addition to the user interface, AppEEARS also provides a publicly accessible API.
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
- Fast Track Your Data Extraction Experience with AppEEARS! AGC 2020
Earth Observation Data by Sensor
ECOSTRESS
NASA's ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), installed on the International Space Station in June 2018, measures the temperature of plants to better understand how they respond to the stress of insufficient water availability. Learn more in this YouTube video: Monitoring Plant Health from Space: NASA’s ECOSTRESS Mission.
Research quality ECOSTRESS ET data products can be accessed directly using Earthdata Search or the Data Pool at LP DAAC. Datasets are available in HDF format but, in some cases, customizable to GeoTIFF:
- ECOSTRESS ET from Earthdata Search
- ECOSTRESS ET at LP DAAC
- All process levels for ECOSTRESS v001 data products are distributed as swath. Typically Level 3 and Level 4 would imply the data are mapped to a grid, but this is not the case with ECOSTRESS. However, if users access ECOSTRESS via AppEEARS those data will be returned as gridded data
MODIS
MODIS aboard NASA's Terra and Aqua satellites yields data that are used to estimate global terrestrial evapotranspiration using the MOD16 global evapotranspiration product.
- MODIS Evapotranspiration data from Earthdata Search
- MODIS ET at LP DAAC
- Early Warning MODIS-based ET data
TM, ETM+, OLI/TIRS
The Landsat Provisional Actual Evapotranspiration (ETa) product can be used to better understand the spatiotemporal dynamics of water use over land surfaces. Provisional ETa science products are available globally for Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) scenes that can be successfully processed to Landsat Collection 2 Level-2 Surface Temperature products.
The Landsat 7 and 8 satellites orbit Earth every eight days. Each satellite carries a thermal sensor that can measure the temperature of everything they pass over. The sensor swath is 115 miles (185 kilometers) and the data in these images are provided at a resolution of about a quarter of an acre, or roughly about the size of a baseball infield. By combining these temperature measurements with other satellite and weather data, scientists can calculate how much evapotranspiration is taking place. This provides an orbital view of how much water is being used across the landscape and allows information to be provided for individual fields and farms just about once a week.
Research quality land surface reflectance data products can be accessed directly using Earthdata Search:
Model Data
OpenET
OpenET is a web-based platform that provides ET data for 17 states in the Western U.S. It uses publicly-available data and open-source models to deliver satellite-based ET information in areas as small as a quarter of an acre at daily, monthly, and yearly intervals. Learn more about OpenET in this YouTube video: OpenET: Transforming Water Management in the American West.
MODIS
Research quality MODIS Level 4 ET products are available in yearly and 8-day temporal resolutions with 500 m pixel size:
LDAS
NASA's Land Data Assimilation System (LDAS) provides model-based ET data and includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses measurements of precipitation, soil texture, topography, and leaf area index (LAI) to model soil moisture and ET. When calculating ET, there are biases around seasonality or local-specific effects, but the model developers try to account for these and calibrate accordingly. Estimates of ET are provided every day and integrated to get monthly, seasonal, or annual information.
GLDAS data products can be visualized using a NASA interactive data analysis tool called Giovanni:
- GLDAS ET in Giovanni
- Data are available with a temporal resolution of 3-hourly, daily, and monthly
Research quality ET data are available through NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC):
Land cover data help quantify land degradation caused by deforestation for agriculture and livestock production. NASA data are used, with other data, as tools for sustainable land management strategies to maintain vegetative cover and health.
Commonly Used Land Cover/Crop Extent Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Covrage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
500 m | Global | 1-2 days |
2001-2021 |
Terra and Aqua | MODIS | Observation | HDF-EOS |
30 m, 90 m, 1 km | Regional datasets | Multi-year | 2007-2016 (varies) |
Derived from Landsat data | GFSAD | Observation | GeoTIFF |
30 m | Global | Yearly | 2001-2019 | Derived from Landsat data | GLanCE | Observation | GeoTIFF |
Use the Data
- Use Cases and Articles
-
- User Profile: Dave Johnson
- User Profile: Dr. Pinki Mondal
- User Profile: Dr. Navin Ramankutty
- Protecting Farmers' Livelihoods Using Satellite Imagery
- Measuring the World's Croplands
- Falling for Corn
- CropHarvest: A global dataset for crop-type classification
- Supporting Food Security in Africa Using ML with Catherine Nakalembe
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Accessing ORNL DAAC OGC services in popular GIS tools
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- The USDA interactive CropScape tool provides crop-specific land cover data layers created annually for the continental U.S. using moderate resolution satellite imagery, specifically from Landsat, and extensive agricultural validation from ground-based measurements
- The USDA Crop Explorer provides global information by region or by crop commodity
- NASA Harvest, is a near real-time monitoring of global croplands that enables global users to track crop conditions as growing seasons unfold
- The Global Agriculture Monitoring system 2 (GLAM 2), developed by NASA Harvest
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- For more information on a suite of NASA data tools for searching and ordering, data handling, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Harvest GitHub repositories
- CropHarvest is an open source remote sensing dataset for agriculture with benchmarks that collects data from a variety of agricultural land use datasets and remote sensing products
- Crop-Mask is an end-to-end workflow for generating high resolution cropland maps
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS. In addition to the user interface, AppEEARS also provides a publicly accessible API.
- Global Agriculture Monitoring System (GLAM) API Documentation
- NASA Earthdata Application Programming Interfaces (APIs)
- NASA Harvest GitHub repositories
Earth Observation Data by Sensor
MODIS
The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Land Cover Type data product (MCD12Q1) provides global land cover types at yearly intervals. This product is derived using supervised classifications of MODIS surface reflectance data. The supervised classifications then undergo additional post-processing that incorporates prior knowledge and ancillary information to further refine specific land type classes.
These data can be accessed and downloaded using NASA Worldview and Earthdata Search:
Global Food Security-support Analysis Data
The Global Food Security-support Analysis Data 30 meter (GFSAD30) collection from NASA NASA MEaSUREs provides global cropland extent data that are divided and distributed into seven separate regional datasets for the year 2015 (2010 for North America) at 30 m resolution. These datasets provide baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.
Another relevant dataset is the Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA). These data are available for the conterminous U.S. (GHISACONUS), and provide dominant crop data (rice, corn, soybeans, cotton, and winter wheat) based on hyperspectral data from the Hyperion instrument aboard NASA's Earth Observing-1 satellite (EO-1). Crop growth states for major agricultural crops are included in the spectral library. GHISACASIA provides dominant crop data (wheat, rice, corn, alfalfa, and cotton) in different growth stages across the Galaba and Kuva farm fields in the Syr Darya river basin in Central Asia.
GSFAD30 and GHISA data are available through Earthdata Search:
GSFAD and GHISA data also are available through NASA’s LP DAAC:
- GFSAD30
- GFSAD30 can be visualized and interactively explored using the USGS GFSAD Cropland Data visualization tool
- GHISACONUS
- GHISACASIA
The USDA's interactive CropScape tool provides crop-specific land cover data layers created annually for the continental U.S. using moderate resolution Landsat satellite imagery and extensive agricultural validation from ground-based measurements. The USDA Crop Explorer provides global information by region or by crop commodity. This service offers advanced tools such as interactive visualization, web-based data dissemination and geospatial queries, and automated data delivery to systems such as Google Earth.
GLanCE
NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30m Version 1 data product provides global land cover and land cover change data derived from Landsat TM, ETM+, and OLI data. These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management.
Leaf Area Index (LAI) is a ratio of leaf surface area to ground surface area. This assigns a quantifiable value to the amount of vegetation on the ground and is an important indicator of the condition and future potential yield of a crop. Knowing the total leaf area in a plant canopy helps scientists determine how much water will be stored and released by an ecosystem, how much leaf litter it will generate, and how much photosynthesis is occurring. These data are used to estimate crop characteristics and extrapolated to predict crop condition and future yield.
Commonly Used Leaf Area Index Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
275 m at all off-nadir angles | Global | Monthly, seasonal | 2007-present | Terra | MISR | Observation | NetCDF-4 |
500 m | Global | 1-2 days | 2000-present | Terra and Aqua | MODIS | Observation | HDF-EOS |
500 m | Global | 1-2 days | 2012-present | Suomi NPP | VIIRS | Observation | HDF-EOS5 |
0.5° x 0.625° | Global | Monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Use Cases and Articles
- Tutorials
-
- Data Access and Visualization of Model Data at the NASA GES DISC
- Exploring Earth’s Land Surface with the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS Land Data
- ORNL DAAC MODIS and VIIRS Data Tools and Services at your Fingertips
- Using NASA’s Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) to Slice and Dice Big Earth Data
- GIS-Ready Tools, Tutorials, and Data
-
- Spatial Data Access Tool (SDAT)Geospatial Data Visualization/Download based on OGC Standards
- Tutorial - Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats.
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service. Web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- Tutorials on GEDI Science Data Products (GEDI L3 and L4)
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
Earth Observation Data by Sensor
MISR
The Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format features LAI. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid.
Global MISR LAI data are available to browse, visualize, and download through the MISR Level 3 Data Browser.
Research quality data are available through Earthdata Search
Model Data
MODIS
MODIS LAI data can be visualized and interactively explored using NASA Worldview:
MODIS LAI Level 4 data are available starting in 2000 through Earthdata Search:
VIIRS
VIIRS LAI Level 4 data are available starting in 2012 through Earthdata Search:
MERRA-2
LAI data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through Giovanni:
MERRA-2 data are available for download in Earthdata Search:
Surface reflectance is a measure of the fraction of incoming solar radiation reflected from Earth's surface to a satellite-borne or aircraft-borne sensor. It is useful for measuring the greenness of vegetation, which can then be used to determine phenological transition dates including the start of the growing season, the period of peak growth, and the end of the growing season. Agricultural production estimates must be restricted to crop-specific areas to avoid confusion with other crops, natural vegetation, and areas of no vegetation. This allows specific crops to be followed through time using sustained land imaging and multi-spectral high-resolution imagery.
Commonly Used Surface Reflectance Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
15 m, 30 m | Global | Variable | 2000-present | Terra | ASTER | Observation | HDF-EOS or GeoTIFF |
500 m, 1 km, 0.05° | Global | 1-2 days | 2000-present | Terra and Aqua | MODIS | Observation | HDF, HDF-EOS2 |
500 m, 1 km, 5,600 m |
Global | 1-2 days | 2017-present | Suomi NPP | VIIRS | Observation | HDF5, HDF-EOS5 |
15 m, 30 m, 60 m | Global | 16 days | 1982-present | Landsat 4, 5, 7, 8, 9 | OLI, OLI-2, ETM+, TM | Observation | GeoTIFF |
30 m | Near-global (no Antarctic) | 2-3 days | 2013-present | HLS |
OLI, OLI-2, MSI |
Observation | Cloud Optimized GeoTIFF (COG) |
Use the Data
- Use Cases and Articles
-
- A Rough Year for Rice in California: Ongoing drought has cut rice acreage in the Sacramento Valley in half
- Data Chat: Dr. Jeffrey Masek: The Harmonized Landsat Sentinel-2 (HLS) project offers daily, 30-meter global land surface data products to facilitate a wide range of terrestrial Earth science research
- A Harmonious New Dataset: The provisional public release of the Harmonized Landsat Sentinel-2 (HLS) dataset through NASA’s LP DAAC opens new avenues for global terrestrial research
- Tutorials
-
- Getting Started with VIIRS Surface Reflectance Data: All about Accessing the Data
- Getting Started with VIIRS Surface Reflectance Data: Using the Data
- Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Accessing the Data
- Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Using the Data
- Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Interpreting Quality Information
- Advancing Science Capabilities with Data Harmonization: NASA's Harmonized Landsat Sentinel-2 Product
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting. LP DAAC also offers an E-Learning section with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS
- Getting Started with Cloud-Native Harmonized Landsat Sentinel-2 (HLS) Data in R
- Jupyter Notebook Tutorial for getting started with HLS data in Python
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
Earth Observation Data by Sensor
MODIS
Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance products provide an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption.
MODIS Surface Reflectance data can be visualized and interactively explored using NASA Worldview:
Multiple Geographic Information Systems (GIS) MODIS Surface Reflectance data layers with different band combinations are available through Esri’s ArcGIS OnLine (AGOL). NASA’s GIS data may be used with open source GIS software such as Quantum GIS or Geographic Resources Analysis Support System (GRASS) GIS at no cost:
Research quality MODIS data products can be accessed directly from Earthdata Search
Near real-time (NRT) MODIS Surface Reflectance data are available through NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) within 60 to 125 minutes after a satellite observation:
ETM+, OLI, OLI-2, TIRS-2
The Enhanced Thematic Mapper (ETM+) and the Operational Land Imager (OLI) aboard the NASA/USGS Landsat 7 (ETM+) spacecraft and Landsat 8 (OLI) spacecraft acquire VNIR data at 30 m spatial resolution every 16 days (fewer days as you move away from the equator). Landsat 9 carries two instruments: the OLI-2 (which is a copy of the Landsat 8 OLI) and the Thermal Infrared Sensor-2 (TIRS-2). TIRS-2 measures land surface temperature in two thermal infrared bands.
Research quality surface reflectance data products can be accessed using USGS EarthExplorer:
- Landsat 7 ETM+
- Landsat 8 OLI
- Landsat 9 OLI-2
HLS
A high-resolution land surface reflectance imagery option is Harmonized Landsat Sentinel-2 (HLS). HLS imagery provide consistent surface reflectance and top of atmosphere brightness data from the Landsat 8 OLI, the Landsat 9 OLI-2, and the Multi-Spectral Instrument (MSI) aboard the ESA (European Space Agency) Sentinel-2A and Sentinel-2B satellites. The harmonized measurement enables global land observations every 2-3 days at 30 m spatial resolution.
- HLS Surface Reflectance imagery in NASA Worldview
- HLS Surface Reflectance imagery from Earthdata Search
ASTER
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument acquires visible and near-infrared (VNIR) reflectance data at 15 m spatial resolution and short wave infrared (SWIR) reflectance data at 30m spatial resolution.
ASTER data can be accessed and downloaded using NASA Worldview:
ASTER is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI). As a tasked sensor, ASTER acquires data when it is directed to do so over specific targets. This makes its temporal resolution variable depending on the requested target region of interest:
Vegetation indices can be used to measure the amount of green vegetation over a given area, which is used as an assessment of vegetation health. Commonly used vegetation indices are the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and EVI2.
The NDVI takes the difference between near-infrared (NIR) and red reflectance divided by their sum: NDVI = (NIR - VIS)/(NIR + VIS). Resulting values range from -1 to 1. Low values of NDVI correspond to low photosynthetic activity (e.g., unhealthy vegetation) or non-vegetated surfaces, such as areas of rock, sand, exposed soils, or snow. Higher NDVI values generally indicate greener, more lush vegetation, including forests, croplands, and wetlands.
The EVI minimizes canopy-soil variations and improves sensitivity over dense vegetation. The EVI2 minimizes atmospheric effects.
Commonly Used Vegetation Greenness Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
250 m, 500 m, 1 km |
Global | 1-2 days | 2000-present | Terra and Aqua | MODIS | Observation | HDF-EOS |
500 m, 1 km, 0.05° |
Global | 1-2 days | 2012-present | Suomi NPP | VIIRS | Observation | HDF-EOS5, HDF-EOS |
0.5° x 0.625° |
Global | Monthly |
1980-present |
N/A | MERRA-2 | Model | netCDF |
Use the Data
- Use Cases and Articles
-
- Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest
- A Rough Year for Rice in California
- How Satellite Maps Help Prevent Another ‘Great Grain Robbery’
- Satellites Help Improve Crop Yields in India
- In Peril: The Freshwater Ecosystem of the Tonlé Sap Basin
- Western Soils and Plants are Parched
- Data User Profile: Dr. Michael Dietze
- Data User Profile: Dr. Nancy Glenn
- Tutorials
-
- ARSET - Remote Sensing of Drought
- Data Access and Visualization of Model Data at the NASA GES DISC
- Getting Started with MODIS Version 6 Vegetation Indices Data : Accessing the Data
- Getting Started with MODIS Version 6 Vegetation Indices Data: Interpreting Quality Information
- Getting Started with MODIS Version 6 Vegetation Indices Data: Using the Data
- Links to LP DAAC resources related to vegetation indices
- Data Visualizations
-
- Animations by NASA's Scientific Visualization Studio (SVS) show NDVI anomalies over time globally and for selected regions:
- GIS-Ready Tools, Tutorials, and Data
-
- Webinar: Creating and Using Normalized Difference Vegetation Index (NDVI) from Satellite Imagery using QGIS
- ARSET - Remote Sensing of Drought
- Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
- Tutorial - Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- Users may access observed and modeled data, as well as visualize those data directly through ClimateSERV. Datasets include Evaporative Stress Index, SMAP Soil Moisture, Rainfall, IMERG, and NDVI
- Landsat Farming Food for All
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- Accessing Data through ORNL DAAC Web Services on Github
- LP DAAC provides a collection of R and Python Data Prep Scripts that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and on how to use AppEEARS
- Global Agriculture Monitoring System (GLAM) API Documentation
- Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
Earth Observations by Sensor
MODIS and VIIRS
Vegetation products created from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and the Visible Infrared Imaging Radiometer Suite (VIIRS) can be accessed in several ways.
Imagery can be interactively explored using NASA Worldview:
- MODIS NDVI data in Worldview
- MODIS EVI data in Worldview
- This dataset is monthly at 1 km spatial resolution
Note: The Terra/MODIS NDVI (rolling 8-day) and EVI (rolling 8-day) are only available in Worldview for the last 20 days; older NDVI or EVI imagery are available using use the Level 3, 16-Day, or Monthly Vegetation Index and EVI layers.
NDVI data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA online interactive data analysis tool called Giovanni:
- MODIS NDVI in Giovanni
- Select a map plot, date range, and region and plot the data; data can be downloaded as GeoTIFF
Multiple Geographic Information Systems (GIS) MODIS Surface Reflectance data layers with different band combinations are available through Esri’s ArcGIS OnLine (AGOL). NASA GIS data may be used with open-source GIS software such as Quantum GIS or Geographic Resources Analysis Support System (GRASS) GIS at no cost:
Research quality vegetation indices can be accessed directly using Earthdata Search (datasets are available as in HDF format but are, in some cases, customizable to GeoTIFF):
NRT MODIS vegetation data can be accessed and downloaded through LANCE.
Additional Visualization Tools
MODIS NDVI Anomaly data are available through the original Global Inventory Modeling and Mapping Studies (GIMMS) Global Agriculture Monitoring System (GLAM) created by the University of Maryland with NASA and the USDA.
The Global Agriculture Monitoring system 2 (GLAM 2) was developed by NASA Harvest: NASA’s Food Security and Agriculture Program. GLAM 2 is a near real-time monitoring of global croplands that enables users to track crop conditions as growing seasons unfold. GLAM 2 offers NDVI along with a suite of meteorological and soil moisture data.
Since GLAM data processing is cloud-based and does not rely on local bandwidth to compile datasets, users can access the publicly available web interface from anywhere in the world. New functions, such as custom time series charts, cropland, and crop type masks, have recently been implemented.
Model Data
MERRA-2
- Model vegetation data are available in Giovanni
- Greenness Fraction
- Select a map plot, date range, and region and plot the data. Data can be downloaded as GeoTIFF
- Greenness Fraction
Water is a key component of the overall Earth system, cycling through each component, moving within the atmosphere, the ocean, the cryosphere (including snow cover and snowpack), surface water of rivers and lakes, and subsurface water. Water availability is critical for human consumption, agriculture and food security, industry, and energy development. Assessing water availability, including the amount and type of precipitation is critical to monitoring agricultural practices and water resource availability and for providing interventions when necessary.
According to the U.N., water use has been growing globally at twice the rate as the global population is increasing. More and more areas are reaching the limit at which water services can be sustainably delivered, especially in arid regions. Groundwater, a major water resource for maintaining cropland productivity, is declining through the extensive use of water for agricultural irrigation, where aquifer recharge cannot keep up with groundwater extraction. Unfortunately, changes in terrestrial water storage, especially with regard to groundwater, are poorly known and sparsely sampled. Complicating matters further, global freshwater is not only unevenly distributed, but sources of freshwater such as lakes and rivers often cross geopolitical boundaries. Integrating satellite data with land-based and other measurements, geospatial data, and hydrologic models help to better understand controls on global water resources and how changing water resources impact social-environmental systems across geopolitical boundaries.
Commonly Used Groundwater Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
380 km2 |
Global | 1 month | 2002-present | GRACE, GRACE-FO | N/A | Observation | netCDF-4 |
0.25° | Global | 7 days | 2003-present | N/A | GRACE, GRACE-FO | Model | netCDF |
0.125° | North America | 7 days | 2002-present | N/A | GRACE-DA-DM | Model | netCDF |
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 1948-almost present | N/A | LDAS | Model | netCDF |
0.5° x 0.625° | Global | Hourly, daily, monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Use Cases and Articles
- Tutorials
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- GRACE(-FO) Data Analysis Tool has been designed to allow for quick-look comparisons and analysis of NASA GRACE datasets. For computational reasons, all data have been interpolated to a 1x1 degree grid. Full datasets can be downloaded through the database for further analysis
- GRACE Interactive Data Analysis and Download Portal allows users to run basic data analysis on Level 2 GRACE data, as well as download maps, and time series plots.
- GRACE/GRACE-FO Mascon in State of the Ocean provides Total Water Storage Anomaly data
- Data Rods Explorer (DRE) enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable. The key variables are precipitation, wind, temperature, surface downward radiation flux, heat flux, humidity, soil moisture, groundwater, runoff, and evapotranspiration. These variables describe the main components of the water cycle over land masses.
- Data Customizing Tools
-
- Soil Moisture Visualizer tool integrates a variety of North American soil moisture datasets. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats.
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page.
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- NASA's Physical Oceanography DAAC (PO.DAAC) has developed a Python script to convert the JPL GRACE Mascon file from NetCDF4 to GeoTIFF format. This GRACE Python script decomposes the multi-year monthly mascon files in NetCDF format into single files in GeoTIFF format for each month
Earth Observation Data by Sensor
GRACE, GRACE-FO
Instruments aboard the joint NASA/German Space Agency Gravity Recovery And Climate Experiment (GRACE, operational 2002 to 2017) and GRACE Follow-On (GRACE-FO, launched in 2018) satellites obtain measurements about changes in Earth's gravity. Since water has mass, changes in groundwater storage can be detected as changes in gravity. GRACE and GRACE-FO measurements help assess water storage changes in monthly, total surface, and groundwater depth.
These data are available from 2002 to present; the data track total water storage time-variations and anomalies (changes from the time-mean) at a resolution of approximately 90,000 km2 and larger. These measurements are unimpeded by clouds and track the entire land water column from the surface down to deep aquifers. GRACE and GRACE-FO data are uniquely valuable for regional studies to determine general trends in land water storage as well as for assessing basin-scale water budgets (e.g., the balance between precipitation, evapotranspiration, and runoff).
The GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height dataset provides gridded monthly global water storage/height anomalies relative to a time-mean. The data are processed at NASA’s Jet Propulsion Laboratory (JPL) using the mascon approach. Mass Concentration blocks (mascons) are a form of gravity field basis functions to which GRACE observations are optimally fit. For more information on this approach, see the JPL Monthly Mass Grids webpage. Data are represented as Water Equivalent Thickness (WET), representing the total terrestrial water storage anomalies from soil moisture, snow, surface water (including rivers, lakes, and reservoirs), as well as groundwater and aquifers.
GRACE and GRACE-FO data can be visualized and interactively explored using tools below. Both products incorporate a Coastal Resolution Improvement filter that reduces leakage errors across coastlines:
Research-quality data products can be accessed using Earthdata Search:
Scientists at NASA's Goddard Space Flight Center use GRACE-FO data to generate weekly groundwater and soil moisture drought indicators. The drought indicators describe current wet or dry conditions, expressed as a percentile showing the probability of occurrence for a specific location and time of year, with lower values (orange/red) indicating drier than normal conditions and higher values (blues) indicating wetter than normal conditions. The drought model is also used to make forecasts of expected drought conditions one, two, and three months into the future.
Model Data
GRACE, GRACE-FO
GRACE Groundwater storage percentile modeled data:
GRACE-DA-DM
Weekly groundwater and soil moisture drought indicators are available each week based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations using a sophisticated numerical model of land surface water and energy processes:
GLDAS
NASA, in collaboration with other agencies, has developed models of groundwater that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS).
GLDAS Groundwater Storage data have spatial resolution of 0.25° and are available at daily temporal resolution. GLDAS data are available from January 1948 to present:
NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) optimally reorganized some large hydrological datasets as time series (also known as data rods) for a set of water cycle-related variables from the NLDAS and GLDAS, the Land Parameter Parameter Model (LPRM), TRMM, and GRACE data assimilation. These are available at GES DISC Hydrology Data Rods.
MERRA-2
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: hourly and monthly from 1980.
MERRA-2 Groundwater model data:
Surface runoff is the volume of available surface water after a precipitation event that has been partitioned into evapotranspiration (ET) and stored as soil moisture. This available water may be used for irrigation, domestic use, and for livestock. Droughts are indicated by lower surface runoff anomaly values showing that water reservoirs have received less than normal amounts of surface runoff. Elevated total runoff anomaly values may indicate flooding events and have lasting impacts on crops, livestock, and residents.
Commonly Used Runoff Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 1948-present | N/A | LDAS | Model | netCDF |
0.5° x 0.625° | Global | Hourly, daily, monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Use Cases and Articles
-
- Shoring up the Corn Belt’s Soil Health With NASA Data
- Quantifying Wintertime Agricultural Land Use and Springtime Management of Winter Cover Crops using Landsat and Sentinel to Support Environmental Conservation in Maryland
- Operational Analysis of Winter Cover Crop Environmental Performance throughout the State of Maryland
- Using NASA Earth Observations to Map Winter Cover Crop Conservation Performance in the Chesapeake Bay Watershed
- Tutorials
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- How to Import MERRA Surface Product Data into ArcGIS
- NASA GIS Tutorials and How-Tos
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- Data Access Tools
-
- Data Rods Explorer (DRE) is a web client app that enables users to browse several NASA-hosted datasets
- AppEEARS offers a simple and effective way to extract, transform, visualize, and download data products; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- NASAaccess is an R package that can generate gridded ASCII tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys)
Model Data
LDAS
Runoff potential is important data for water resources and agricultural management, especially after storm events and wildfires. Runoff can impact water quality as chemicals from fertilizers and stormwater runoff, debris, and waste products enter water bodies. Satellites cannot measure runoff directly; however, information that can be used to predict runoff can be measured remotely. These data are then input, along with ground-based data, into land surface models to estimate runoff.
NASA's Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a North American collection (NLDAS), takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates of runoff and evapotranspiration:
- NLDAS (North American) Runoff Data in Giovanni
- FLDAS (Famine) Runoff Data in Giovanni
- GLDAS (Global) Runoff Data in Giovanni
Research quality LDAS Total Runoff data are available in Earthdata Search:
MERRA-2
Modern-Era Retrospective analysis for Research and Applications version 2 (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. The dataset covers the period of 1980-present with the latency of approximately three weeks after the end of a month:
- MERRA-2 Runoff Data in Giovanni
- Data are in multiple temporal resolutions and multiple temporal coverages; be sure to note the starting and end date to ensure you access the desired dataset
Research quality MERRA-2 Total Runoff datasets are available in Earthdata Search:
Seasonal water runoff from snowpack and glaciers provide irrigation and drinking water for billions of people worldwide. The Indus Basin in Asia, for example, is the largest irrigation system in the world; snow melt from the Himalayan mountains is essential for rice production in the basin and contributes significantly to agricultural irrigation. Changes in global snow cover can have major impacts on food production.
Snow Water Equivalent (SWE) is the amount of water contained in snowpack. It is analogous to melting the snow and measuring the depth of the resulting pool of water. SWE measurements are useful for assessing both the potential surface runoff from snowmelt and the water availability for regions in lower elevations.
Common Snow Cover/SWE Data at a Glance
An asterisk (*) next to an entry indicating that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) within three hours from satellite observation. Imagery is generally available 3-5 hours after observation. While not intended for scientific research, NRT data are good resources for monitoring ongoing or time-critical events.
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
500 m, 1 km, 0.05° | Global |
1-2 days, |
2000-present | Terra and Aqua | *MODIS | Observation | HDF-EOS |
15 km | Global | 2 days | 2012-near present | SHIZUKU (GCOM-W1) | *AMSR2 | Observation | HDF5 |
3 m, 50 m | California, Colorado | Varies | 2013-2019 | N/A | ASO | Observation | GeoTIFF |
1 km | North America, Hawaii, Puerto Rico | Daily | North America, Hawaii: 1980-present Puerto Rico: 1950-present |
N/A | Daymet | Model | netCDF, Cloud Optimized GeoTIFF (COG) |
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 1948-present | N/A | LDAS | Model | netCDF |
0.5° x 0.625° | Global | Hourly, daily, monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Tutorials
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
- Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- Snow Today, available through the National Snow and Ice Data Center (NSIDC), is a NASA-supported scientific analysis website that provides a snapshot and interpretation of snow conditions in near real-time across the Western U.S. Snow Today updates daily images on snow conditions and relevant data and also provides monthly scientific analyses from January to May, or more frequently as conditions warrant
- Data Customizing Tools
-
- Daymet Single Pixel Data Extraction—Web Services
- AppEEARS offers a simple and effective way to extract, transform, visualize, and download Snow cover data products and allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- MODIS/VIIRS Subsetting Tools Suite
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
Earth Observation Data by Sensor
MODIS
Near real-time snow cover data can be interactively explored using NASA Worldview:
Research quality data products can be accessed using Earthdata Search:
- MODIS Snow Cover
- Data are available daily, 8-day, or monthly at various resolutions; datasets are also customizable to GeoTIFF
Near real-time (NRT) MODIS snow cover data are available through LANCE 60 to 125 minutes after a satellite observation.
GIS layer displaying water states (ice, snow, water, etc.):
AMSR2
The Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) instrument and the AMSR-2 instrument provide SWE data.
Near real-time data can be interactively explored using NASA Worldview:
Research quality data products can be accessed using Earthdata Search:
- AMSR-E SWE
- Data are available from 2002 to 2011 in daily, 5-day, and monthly timeframes; datasets are also customizable to GeoTIFF
- AMSR2 SWE
- Data are available from February 2018
Near real-time (NRT) AMSR2 snow water equivalent data are available through LANCE 60 to 125 minutes after a satellite observation.
Model Data
ASO
NASA's Airborne Snow Observatory (ASO) mission collects data on snowmelt flowing out of major water basins in the Western U.S. The mission began in April 2013 as a collaboration between NASA's Jet Propulsion Laboratory (JPL) and the California Department of Water Resources, with weekly flights over the Tuolumne River Basin in California and monthly flights over the Uncompahgre River Basin in Colorado during the snowmelt season. Current data collection is undertaken by Airborne Snow Observatories, Inc., a private company working in partnership with Esri and the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro) team of the National Center for Atmospheric Research.
Daymet
Another NASA source for SWE data is Daymet, which can be accessed through NASA's ORNL DAAC. Daymet is a collection of gridded estimates of daily weather parameters including minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over the North America, Puerto Rico, and Hawaii from 1980 to present (North America and Hawaii) and from 1950 to present (Puerto Rico).
NLDAS, GLDAS, FLDAS
NASA, in collaboration with other agencies, has developed models of snow cover and depth that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS).
Snow depth data products are are available from Giovanni. These data can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using an online interactive data analysis tool called Giovanni:
- Snow Depth
- Snow Water Equivalent
GLDAS model data are available from NASA's GES DISC:
MERRA-2
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: hourly and monthly from 1980.
MERRA -2 Snow data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:
Soil moisture is vital for crop growth and yield, while extreme values (high or low) can negatively impact crop growth. Timely seasonal soil moisture information enhances food security and provides the ability to detect drought and other water-related stressors (such as root rot) on crop production. Soil moisture datasets indicate the amount of water at a specific location in the soil profile, based either on depth or infiltration time.
Commonly Used Soil Moisture Data at a Glance
An asterisk (*) next to an entry indicating that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) within three hours from satellite observation. Imagery is generally available 3-5 hours after observation. While not intended for scientific research, NRT data are good resources for monitoring ongoing or time-critical events.
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
9-40 km | Near-global | Daily, 3-day | 2015-present | SMAP | *Radar (active; no longer operational), Microwave Radiometer (passive) | Observation and Model | HDF5 |
25 km | Global | 50 minutes | 2012-near present | SHIZUKU (GCOM-W1) | *AMSR-2 | Observation | HDF-EOS5 |
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 1948-present | N/A | LDAS | Model | netCDF |
3 km | CONUS, Alaska, East Africa | Daily | 2003-2021 | N/A | SPoRT-LiS | Model | netCDF |
0.5° x 0.625° | Global | Hourly, daily, monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
9 km | Global | 3 hour | 2015-present | N/A | GMAO SMAP | Model | HDF5 |
0.125° | North America | 7 days | 2002-present | N/A | GRACE-DA-DM | Model | netCDF |
Use the Data
- Use Cases and Articles
- Tutorials
-
- ARSET - Water Resource Management Using NASA Earth Science Data
- Learn how to Search, Order, and Customize SMAP Data using Earthdata Search (PDF)
- ARSET - Satellite Remote Sensing for Agricultural Applications
- ARSET - Applications of Remote Sensing to Soil Moisture and Evapotranspiration
- NASA's Soil Moisture Active Passive (SMAP) Mission Data Products
- SPORT Training Module: NASA Land Information System (LIS): Soil Moisture Percentile
- Background information in training modules:
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- How to Import MERRA Surface Product Data into ArcGIS
- Visualize NSIDC data as WMS layers with ArcGIS and Google Earth
- How to Import SMAP HDF Data Into ArcGIS
- Accessing ORNL DAAC OGC services in popular GIS tools
- How to Import AMSR-E Daily Soil Moisture data into ArcGIS
- Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- Crop Condition and Soil Moisture Analytics (Crop-CASMA) is a freely available online tool developed by the USDA's National Agricultural Statistics Service, NASA, and George Mason University that provides access to high-resolution data from SMAP (and MODIS) in an easy-to-use format
- Users can visualize data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, Rainfall, and NDVI
- SMAP Soil Moisture Level 3, 9KM Day and Night Data are available through the State of the Ocean (SOTO) Visualization Tool
- Soil Moisture Visualizer Guide
- Data Rods Explorer (DRE) enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable. The key variables are precipitation, wind, temperature, surface downward radiation flux, heat flux, humidity, soil moisture, groundwater, runoff, and evapotranspiration. These variables describe the main components of the water cycle over land masses
- SMAP Data Access Tool
- Programmatic Data Access Guide (SMAP)
- Data Customizing Tools
-
- Soil Moisture Visualizer integrates a variety of North American soil moisture datasets. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats.
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow.
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- Analysis of Soil Moisture at USFS Rangeland Monitoring Sites with Python
- NASA Earthdata Application Programming Interfaces (APIs)
- Download and Visualize SMAP Data using Python
- Learn how to work with SMAP data at NSIDC DAAC
- ClimateSERV offers an API for those who wish to incorporate their data into a separate application or script
Earth Observation Data by Sensor
SMAP
NASA's Soil Moisture Active Passive satellite (SMAP, launched in 2015) measures the moisture in the top 5 cm of soil globally daily and every 2-3 days at a resolution of 9-36 km.
Near real-time SMAP imagery can be interactively explored using NASA Worldview:
- SMAP in Worldview
- includes root zone and surface soil moisture values
Research quality data products can be accessed using Earthdata Search:
Near real-time (NRT) SMAP data are available through NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) within 60 to 125 minutes after a satellite observation:
AMSR-2
The Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) instrument and the AMSR-2 instrument provide volumetric soil moisture data. AMSR2 provides global passive microwave measurements of Surface Soil Moisture. Near real-time (NRT) products are generated within 3 hours of the last observations in the file.
Data can be visualized using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using a NASA interactive tool called Giovanni:
Near-real time data:
- AMSR-2 NRT data are available through LANCE (3 hours, global, from 2018 to present)
Research-quality data products can be accessed using Earthdata Search:
Model Data
SPoRT-LiS
The Short-term Prediction Research and Transition (SPoRT) project at NASA's Marshall Space Flight Center in Huntsville, AL, is a NASA- and NOAA-funded activity to transition experimental/ quasi-operational satellite observations and research capabilities to the operational weather community to improve short-term weather forecasts on a regional and local scale.
Short-term Prediction Research and Transition-Land Information System (SPoRT-LiS) provides real-time 3km Land Information System data on the following parameters: Volumetric Soil Moisture, Relative Soil Moisture, Column-Integrated Relative Soil Moisture, and Green Vegetation Fraction.
SPoRT offers a Near Real-Time Viewer that includes SMAP datasets for the following regions:
- ENH Alaska (9km Enhanced)
- ENH Continental U.S. (CONUS) (9km Enhanced)
- ENH East Africa (9km Enhanced)
- L2 CONUS (Level 2)
- L2 East Africa (Level 2)
LDAS
NASA, in collaboration with other agencies, has developed models of soil moisture content that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and uses these inputs to model output estimates of soil moisture and evapotranspiration.
The NLDAS experimental drought monitor is derived from near real-time soil moisture output model data:
Soil MERGE (SMERGE) is a root-zone soil moisture product developed by merging NLDAS land surface model output with surface satellite retrievals from the ESA (European Space Agency) Climate Change Initiative. This data product contains root-zone soil moisture of 0-40 cm layer, Climate Change Initiative (CCI)-derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag:
NLDAS, GLDAS, and SMERGE data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:
LDAS Soil Moisture data from GLDAS, NLDAS and FLDAS are available in Earthdata Search:
GRACE-DA-DM
Weekly soil moisture and groundwater drought indicators are available each week based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes, referred to as GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM):
GMAO SMAP
NASA’S Global Modeling And Assimilation Office (GMAO), in collaboration with The University of Montana and the NASA Jet Propulsion Laboratory, provides value-added Level-4 data products. These Level-4 datasets rely on the merger of SMAP observations into physically-based numerical models of the land surface water, energy, and carbon cycles. Available Level-4 data include global, 9-km, 3-hourly estimates of surface and root zone soil moisture, surface and soil temperature, and land surface fluxes, along with algorithm diagnostics from the ensemble-based data assimilation system. Level-4 data further include global, 9-km, daily estimates of net ecosystem CO2 exchange, component carbon stocks and fluxes, and sub-grid information broken down by plant functional types.
Near real-time SMAP imagery can be interactively explored using NASA Worldview:
These data products are available from Earthdata Search:
MERRA-2
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: hourly and monthly from 1980.
MERRA -2 Soil Moisture data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:
MERRA-2 Soil Moisture data in Earthdata Search:
Water budgets for individual watersheds can be estimated using remote sensing data for precipitation, evapotranspiration, and runoff.
Commonly Used Water Reservoir Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Covrage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
N/A | Global | 8-day, monthly | 2000-present | Terra | MODIS | Observation | HDF |
N/A | Global | 8-day, monthly | 2002-present | Aqua | MODIS | Observation | HDF |
Use the Data
- Use Cases and Articles
-
- An Outpouring of Color
- Lake Observations by Citizen Scientists and Satellites (LOCSS) is a citizen science program designed to better understand how the water volume in lakes is changing
- Tutorials
-
- ARSET - Water Resource Management Using NASA Earth Science Data
- ARSET - Using Earth Observations to Monitor Water Budgets for River Basin Management
- ARSET - Using Earth Observations to Monitor Water Budgets for River Basin Management II
- ARSET - Mapping and Monitoring Lakes and Reservoirs with Satellite Observations
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
- Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats.
- MODIS/VIIRS Subsetting Tools Suite
- Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
- Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
- Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
- For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- LakePy is a Python-based front-end to the Global Lake Level Database, which provides lake water levels for more than 2,000 lakes worldwide. Data come from three sources: the USGS National Water Information System, the USDA Foreign Agricultural Service's Global Reservoirs and Lakes Monitor Database, and Theia's Hydroweb Database. The site contains a walk-through Jupyter Notebook
Earth Observation Data by Sensor
MODIS
Terra and Aqua MODIS Water Reservoir data products provide a monthly time series of reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume for 151 human-created reservoirs and 13 regulated natural lakes worldwide. These data products are available through Earthdata Search:
GIS layer displaying water states (ice, snow, water, etc.):
The Global Reservoir and Dam (GRanD) dataset, provided by NASA’s Socioeconomic Data and Applications Center (SEDAC),displays the location of dams and water reservoirs around the world as feature points:
For more information on additional data resources on water resources, visit the Sea Level Change Data Pathfinder.
NASA satellites, airplanes, and ground stations collect data on events like rainfall, drought, and other extreme weather events—data that are critical for informing agricultural decisions and policy.
Humidity is a measure of the amount of water vapor present in the air. High humidity impairs heat exchange efficiency by reducing the rate of moisture evaporation from the skin and other surfaces. This can create challenges for agricultural workers, as well as the crops they grow.
Commonly Used Humidity Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
1° | Global | Daily, weekly, monthly | 2002-present | Aqua | AIRS | Observation | HDF-EOS |
0.5° x 0.625° | Global | Hourly, daily, monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Tutorials
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
- Data Rods Explorer (DRE) enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable
- GEOS-5 Weather Maps: Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour; for variables near the surface, make sure to select 850 mb as your level
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- For information on a suite of NASA data tools for searching and ordering, data handling, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
Earth Observation Data by Sensor
AIRS
Data, often in near real-time (NRT), can be visualized using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using a NASA interactive tool called Giovanni:
Research-quality data products can be accessed using Earthdata Search:
Model Data
MERRA-2
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly, daily, and monthly. These options provide information on precipitation.
Data, often in near real-time (NRT), are available for visualization using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using a NASA interactive tool called Giovanni:
Research-quality data products can be accessed using Earthdata Search:
- MERRA-2 Humidity in Earthdata Search
- There are several options available: 1-hourly, 3-hourly, 6-hourly; these options provide information on surface specific humidity, specific humidity at 2 m, and relative humidity
The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing. The data are also available via API, GIS enabled, as well as via OPeNDAP.
- POWER provides Specific Humidity and Relative Humidity at 2 meters
Rain and snow provide the water upon which agriculture depends, either directly (via rain or snowfall directly on fields) or indirectly (through water reserves that are used for irrigation). Understanding how this water is distributed and how it changes is essential to food security and sustainable water usage.
Commonly Used Precipitation Data at a Glance
An asterisk (*) next to an entry indicating that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) within three hours from satellite observation. Imagery is generally available 3-5 hours after observation. While not intended for scientific research, NRT data are good resources for monitoring ongoing or time-critical events.
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform |
Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
15 km | Global | 2 days | 2012-near present | SHIZUKU (GCOM-W1) | *AMSR-2 | Observation | HDF5 |
0.5° | Global | Daily | 1983-2021 | GPCP | Varies | Observation | netCDF |
0.1° | Global | 30-minute, daily, monthly | 2000-present | GPM IMERG | Varies | Observation |
HDF, netCDF, |
5 km | 50° N to 50° S, 180° W to 180° E | 5-day, 10-day, 15-day | 2000-present | N/A | CHIRPS-GEFS | Model | GeoTIFF |
1 km | North America, Hawaii, Puerto Rico | Daily |
North America, Hawaii: 1980-present Puerto Rico: 1950-present |
N/A | Daymet | Model | netCDF, Cloud Optimized GeoTIFF |
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 2000-2022 | N/A | LDAS | Model | netCDF |
0.5° x 0.625° | Global | Hourly, daily, monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Use Cases and Articles
-
- Analyzing and Assessing Drought Conditions for Crops across Alabama Using NASA Earth Observations
- Crop Monitoring and Forecasting for Argentina Using NASA Satellite Observations
- Derecho Flattens Iowa Corn
- NASA at Your Table: Climate Change and Its Environmental Impacts on Crop Growth
- GES DISC Data in Action: Combining NASA Precipitation Data With GIS to Assess Hurricane Ida Flood Impact
- GES DISC Data in Action: Precipitation data provides a detailed view of extraordinary rainfall event in Australia (nasa.gov)
- Blending CHIRPS Data And GEFS Forecasts For An Enhanced Rainfall Forecast Product
- Tutorials
-
- ARSET - Applications of GPM IMERG Reanalysis for Assessing Extreme Dry and Wet Periods
- ARSET - Water Resource Management Using NASA Earth Science Data
- ARSET - Introduction to Global Precipitation Measurement (GPM) Data and Applications
- Data Access and Visualization of Model Data at the NASA GES DISC
- NetCD-what? An Ecologist’s Guide to Working with Daymet and other NetCDF-formatted Data
- International Precipitation Working Group (IPWG) and Global Precipitation Measurement (GPM) Applications Training
- Introductory Webinar: Overview and Applications of Integrated Multi-Satellite Retrievals for GPM (IMERG) Long-term Precipitation Data Products
- Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) Precipitation Products and Services at GES DISC
- GES DISC How-To's: How to access the GES DISC IMERG ArcGIS Image Service using the ArcGIS Enterprise Map Viewer (nasa.gov)
- All IMERG/GPM Trainings
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
-
- Tutorial: How to Import MERRA Surface Product Data into ArcGIS
- Tutorial: Toward Analysis Ready Data - Programmatically Discover, Access, and Subset Daymet V4 Data with Python and ArcGIS
- Tutorial: ArcGIS Image Service for IMERG Early and IMERG Final
- Tutorial: Using NASA’s GPM IMERG ArcGIS Image Service in ArcGIS Pro
- Tutorial: Using NASA’s GPM IMERG ArcGIS Image Service in Jupyter Notebook: Assessing Hurricane Impact
- Tutorial: Using NASA’s GPM IMERG ArcGIS Image Service in a Jupyter Notebook: How to find the max precipitation value of a Region of Interest (ROI)
- Tutorial: How to Import IMERG GPM Precipitation Data in HDF5 into ArcGIS with Arcpy Script
- Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- Users may access and visualize these data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, SMAP Soil Moisture, Rainfall, and NDVI
- Data Rods Explorer (DRE) interface enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable
- Data Customizing Tools
-
- Daymet Single Pixel Data Extraction—Web Services
- Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide
- AMSR-2 Tools and Libraries
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- How to use AMSR Earth Observation Data
- For information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- Toward Analysis Ready Data—Programmatically Discover, Access, and Subset Daymet V4 Data with Python and ArcGIS
- Agrometeorological data using R-software
- DaymetPy: A python library for accessing Daymet surface weather data
- A Programmatic Interface to the Daymet Web Services: DaymetR
- AMSR-2 Libraries
- NASAaccess is an R package that can generate gridded ASCII tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys)
- ClimateSERV offers an API for those who wish to incorporate their data into a separate application or script
Earth Observation Data by Sensor
AMSR-2
The Advanced Microwave Scanning Radiometer 2 (AMSR-2) instrument collects data that indicate the rate at which precipitation is falling on the ocean surface in millimeters per hour (mm/hr).
Data can be visualized using NASA Worldview:
Research-quality data products can be accessed using Earthdata Search:
Near real-time (NRT) AMSR-2 Surface Precipitation products are generated by the Land Atmosphere Near real-time Capability for EOS (LANCE). NRT products are generated in HDF-EOS-5 augmented with netCDF-4/CF metadata and are available via HTTPS from LANCE. If data latency is not a primary concern, please consider using science quality products, which are created using the best available ancillary, calibration, and ephemeris information.
GPM
NASA's Precipitation Measurement Missions (PMM) provide a continuous record of precipitation data through the Tropical Rainfall Measuring Mission (TRMM; operational 1997 to 2015) and the Global Precipitation Measurement mission (GPM; launched in 2014). GPM, the TRMM successor mission, provides more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.
IMERG
Data products from TRMM and GPM are available individually and have been integrated with data from a global constellation of satellites to yield precipitation estimates with improved spatial coverage and temporal resolution. The first integrated product was the TRMM Multi-satellite Precipitation Analysis (TMPA), which has been superseded by the Integrated Multi-satellitE Retrievals for GPM (IMERG).
IMERG's multiple runs accommodate different user requirements for accuracy and latency (Early = 4 hours, e.g., for flash flood events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research). Along with Earthdata Search, IMERG data are available through NASA's GPM website.
GPM data can be visualized using NASA Worldview:
The Short-term Prediction Research and Transition (SPoRT) project at NASA's Marshall Space Flight Center in Huntsville, AL, is a NASA- and NOAA-funded activity to transition experimental/ quasi-operational satellite observations and research capabilities to the operational weather community to improve short-term weather forecasts on a regional and local scale. SPoRT offers a Near Real-Time Viewer for IMERG data:
- GPM IMERG Early
- GPM IMERG Late
- IMERG - Tropics
Users may access and visualize these data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, SMAP Soil Moisture, Rainfall, and NDVI.
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through Giovanni:
Near-real time (NRT) data:
- IMERG Early Run Half-Hourly
- The "early run" product at NASA's Global Precipitation Measurement website is generated every half hour with a 6-hour latency from the time of data acquisition
Research-quality data products can be accessed using Earthdata Search:
- TMPA
- Rainfall estimates at 3 hours, 1 day, or NRT and accumulated rainfall at 3 hours and 1 day
- IMERG
- Early, Late, and Final precipitation data on the half-hour or 1-day timeframe
Model Data
CHIRPS-GEFS
SERVIR (a joint initiative of NASA, USAID, and geospatial organizations in Asia, Africa, and Latin America) and the Climate Hazards Group (CHG) at University of California at Santa Barbara have developed an improved rainfall forecast dataset that merges two highly recognized datasets: Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and the NCEP’s Global Ensemble Forecasting System (GEFS). GEFS is a weather forecast system that provides daily forecasts out to 16 days at 1 X 1 degree resolution at 6-hour intervals.
The combined CHIRPS-GEFS dataset uses the higher spatial resolution of CHIRPS and the advanced forecasting ability of GEFS to provide up to a 16-day forecast updated every five days at a global spatial resolution of 5 km. CHIRPS-GEFS model data are available for analysis and download through SERVIR's Product Catalog. Users may access and visualize these data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, SMAP Soil Moisture, Rainfall, and NDVI.
Daymet
Another NASA source for precipitation data is Daymet, which can be accessed through NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Daymet is a collection of gridded estimates of daily weather parameters including minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii. Daymet data are available from 1980 to present (North America and Hawaii) and from 1950 to present (Puerto Rico) and can be retrieved in a variety of ways, including: Earthdata Search; an API available through ORNL DAAC; ORNL DAAC tools; and through the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) application. Along with daily data, annual Daymet climatologies also are available.
LDAS
The Land Data Assimilation System (LDAS) includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses measurements including air temperature, precipitation, soil texture, topography, and leaf area index to model high quality fields of land surface states (e.g., soil moisture, temperature) and fluxes (e.g., evapotranspiration, runoff).
GLDAS modeled data are available from January 1948 to present. NLDAS modeled datasets are available from January 1979 to present.
Data can be visualized using NASA Worldview:
MERRA-2
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly, daily, and monthly. These options provide information on precipitation.
Data, often in near real-time (NRT), are available for visualization using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:
Research-quality air surface temperature data products can be accessed using Earthdata Search:
The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing. The data are also available via API as well as via OPeNDAP.
Surface Air Temperature (SAT) generally refers to the air temperature generally measured at approximately 6.5 feet (about 2 meters) above the ground or ocean surface. Surface air temperature provides a key indicator of climate change, contributing to the “global surface temperature record”. SAT (along with other factors like soil moisture, soil type, etc.) affects soil temperatures, which can impact crop development and productivity.
Commonly Used Surface Air Temperature Data at a Glance
Spatial Resolution | Spatial Coverage | Temporal Resolution | Temporal Coverage | Satellite/ Platform | Sensor(s)/ Model Name | Observation or Model | File Format |
---|---|---|---|---|---|---|---|
1° | Global | 3 hours, 12 hours, daily, monthly | 2002-present | Aqua | AIRS | Observation | HDF-EOS |
1 km | North America, Hawaii, Puerto Rico | Daily |
North America, Hawaii: 1980-present Puerto Rico: 1950-present |
N/A | Daymet | Model | netCDF, Cloud Optimized GeoTIFF |
0.01°, 0.1°, 0.125°, 0.25°, 1° | Global | Hourly, 3-hourly, daily, monthly | 1948-present | N/A | LDAS | Model | netCDF |
0.5° x 0.625° | Global | Monthly | 1980-present | N/A | MERRA-2 | Model | netCDF |
Use the Data
- Tutorials
- Data Visualizations
-
- How Does NASA Model Atmospheric Patterns?
- Impact of Climate Change on Global Agricultural Yields
- Climate Change Could Affect Global Agriculture within 10 Years
- Impact of Climate Change on Global Maize Yields
- Impact of Climate Change on Global Wheat Yields
- Increasingly Dangerous Climate for Agricultural Workers
- GIS-Ready Tools, Tutorials, and Data
-
- Tutorial: GES DISC How To’s
- HEG: HDF-EOS to GeoTIFF Conversion Tool
- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder.
- NASA GIS Tutorials and How-Tos
- Data Access Tools
-
- Data Rods Explorer (DRE) is a web client app that enables users to browse several NASA-hosted datasets; key variables are precipitation, wind, temperature, surface downward radiation flux, heat flux, humidity, soil moisture, groundwater, runoff, and evapotranspiration
- GEOS-5 Weather Maps: Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour; for variables near the surface, make sure to select 850 mb as your level
- Data Customizing Tools
-
- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- For information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
-
- NASA Earthdata Application Programming Interfaces (APIs)
- GES DISC Surface Air Temperature - How To’s
- NASAaccess is an R package that can generate gridded ASCII tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys)
- ORNL DAAC tools for accessing Daymet data products
Earth Observation Data by Sensor
AIRS
The Atmospheric Infrared Sounder (AIRS) provides 3D measurements of temperature, water vapor, trace gases, and surface and cloud properties through the atmospheric column.
Data, often in near real-time (NRT), are available for visualization using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:
Research-quality air surface temperature data products can be accessed using Earthdata Search:
AIRS NRT data are available from NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) 75 to 140 minutes after a satellite observation.
Model Data
LDAS
The Land Data Assimilation System (LDAS) includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses measurements including air temperature, precipitation, soil texture, topography, and leaf area index to model high quality fields of land surface states (e.g., soil moisture, temperature) and fluxes (e.g., evapotranspiration, runoff).
GLDAS and NLDAS
GLDAS data are available from January 1948 to present. Retrospective hourly/monthly NLDAS datasets are available from January 1979 to present.
Monthly data can be visualized using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:
FLDAS
The Famine Early Warning Systems Network (FEWS NET) provides early warning and analysis on food insecurity. Created by the U.S. Agency for International Development (USAID) to help decision-makers plan for humanitarian crises, FEWS NET provides evidence-based analysis on more than 30 countries.
The FEWS Net Land Data Assimilation System (FLDAS) is a custom instance of NASA's Land Information System (LIS) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing-countries. The integration of LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, evapotranspiration (ET), and other variables based on multiple meteorological inputs or land surface models.
FLDAS data can be visualized as time-averaged maps, animations, seasonally-averaged maps, scatter plots, or time series using a NASA interactive data analysis tool called Giovanni:
MERRA-2
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface skin temperature, air temperature at 2 m, and air temperature at 10 m.
Data, often in near real-time (NRT), are available for visualization using NASA Worldview:
Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:
Research-quality air surface temperature data products can be accessed using Earthdata Search:
Daymet
Another NASA source for air temperature modeled data is Daymet. Daymet is a collection of gridded estimates of daily weather parameters including minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii. Daymet data are available from 1980 to present (North America and Hawaii) and from 1950 to present (Puerto Rico).
Research-quality air surface temperature data products can be accessed using Earthdata Search:
Weather maps produced from model data and satellite observations can be used to create medium and long-range forecasts of temperature, humidity, precipitation, and more.
To support NASA’s satellite missions and field experiments, NASA's Global Modeling and Assimilation Office (GMAO) generates near-real time atmospheric products using model data from the Goddard Earth Observing System (GEOS) and distributes them to a broad community of users. The GEOS "Forward Processing" (FP) system generates analyses, assimilation products, and 10-day forecasts with the most up-to-date validated version of GEOS that is available. Available forecast parameters include precipitation, humidity, wind speed, and temperature.
- GEOS-5 Weather Maps
Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour. Animate shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For those variables near the surface, make sure to select 850 mb as your level.
Visit the sections below to find resources that help you explore weather maps and models.
Use the Data
- Use Cases and Articles
- Data Visualizations
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- How NASA Satellites Help Model the Future of Climate
- From Observations to Models
- How Does NASA Model Atmospheric Patterns?
- Evolution of the Meteorological Observing System in the MERRA-2 Reanalysis
- GRACE Data Assimilation and GEOS-5 Forecasts
- Global Carbon Dioxide 2020-2021 for Hyperwalls
- Simulated Clouds and Precipitable Water
- Simulated Atmospheric Carbon Concentrations
- How Does NASA Model Atmospheric Patterns?
- GIS-Ready Tools, Tutorials, and Data
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- The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing. The data are also available via API, GIS enabled, as well as via OPeNDAP. POWER provides climate zone data
- NASA Tropical Storms Web Map: Tropical Storms Web Map - Overview (arcgis.com)
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- NASA GIS Tutorials and How-Tos
- Data Access Tools
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GEOS-5 Weather Maps: Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour
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- Data Customizing Tools
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- AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
- For information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page
- Programming Tools
An estimated 720 and 811 million people in the world faced hunger in 2020, according to the United Nations (UN), and nearly one in three people in the world (2.37 billion) did not have access to adequate food in 2020. The vulnerabilities and inadequacies of global food systems are expected to further intensify over the coming years. The combination of NASA Earth science data with socioeconomic data provides key information for sustainable use of available resources.
NASA's Socioeconomic Data and Applications Center (SEDAC) is the home for NASA socioeconomic data and is a gateway between the social sciences and the Earth sciences. SEDAC provides numerous datasets and data collections that may be useful for studies into agriculture and water management. SEDAC also provides information about the connections that support efforts to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture.
NASA helps develop tools to address food security and works with decision-makers and data users to tailor these tools to specific locations and user needs. These efforts help address issues like water management for irrigation, crop-type identification and land use, coastal and lake water quality monitoring, drought preparedness, and famine early warnings.
Much of this work is carried out and supported fully or in part by the agency's Applied Sciences Program, which works with individuals and institutions worldwide to inform decision-making, enhance quality of life, and strengthen the economy. The Applied Sciences Program co-leads the international Earth Observations for Sustainable Development Goals initiative, which advances global knowledge about effective ways that Earth observations and geospatial information can support the SDGs.
The NASA datasets and resources listed below, coupled with other data and resources in this Data Pathfinder, also help measure progress toward meeting United Nations’ Sustainable Development Goals (SDGs), particularly SDG 2: Zero Hunger. These data can provide a better overall view for monitoring the food insecurity of vulnerable populations, tracking agricultural production related to incomes of small-scale food producers, and monitoring environmental impacts to soil, water, fertilizer, pesticide pollution, and changes in biodiversity. More information is available in the Connection of Sustainable Development Goals to Agriculture and Water Management section on the main Pathfinder landing page.
Agriculture and Human Dimensions
- Agriculture and Food Security theme landing page
- Global Agricultural Inputs, v1
- The five datasets in this data collection provide global gridded data and maps on pesticide application, phosphorus in manure and chemical fertilizers, and nitrogen in manure and chemical fertilizers
- Global Pesticide Grids (PEST-CHEMGRIDS), v1.01 (2015, 2020, 2025)
- Global coverage; 5 arc-min spatial resolution; GeoTIFF, netCDF-4
- Web Map Service Layers
Food Supply
- Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios, v1 (1970 – 2080)
- Global coverage; national resolution; .xlsx
- Web Map Service Layers
- Food Insecurity Hotspots Data Set, v1 (2009 – 2019)
- Global coverage; national resolution; GeoTIFF, Shapefile
- Web Map Service Layers
- Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 – 2050)
- Allows users to understand how slow-onset climate change impacts on water availability and crop productivity, coupled with sea-level rise and storm surge, may affect the future population distribution and climate-related internal migration in low to middle income countries
Crop Production
- Twentieth Century Crop Statistics, v1 (1900 – 2017)
- Global coverage (selected countries); national/sub-national resolution; annual
Population
- Global Population Projection Grid Data
- Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 – 2050)
Climate Change Impact
- Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios, v1 (1970 – 2080)
- Global coverage; national resolution; .xlsx
- Web Map Service Layers
- Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 – 2050)
Environmental Performance
- 2022 Environmental Performance Index
- Global coverage; national resolution; .xlsx, csv
- 15 static maps
Poverty
Use the Data
- Use Cases and Articles
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- No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework
- Proving the Value of Earth Observation Data for Small-Scale Agriculture
- Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning
- NASA Addressing Global Challenges: Food Security
- Improving Food Security Through Capacity Building
- Space For Ag: Nasa Works To Ensure Food Security
- Tutorials
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- ARSET - Earth Observations for Monitoring the UN Sustainable Development Goals aims to show the potential and current applications of Earth observations and geospatial information for monitoring the UN SDGs
- Satellite Remote Sensing for Agricultural Applications addresses how to use remote sensing data for agriculture monitoring, specifically drought and crop monitoring
- Gridded Population and Settlement Data: An Introduction to the POPGRID Data Collaborative
- Mapping Global Urbanization from Landsat Data and High-Resolution Reference Data
- Introduction to the SEDAC Population Estimation Service and Mapping Tool
- Remote Sensing Derived Environmental Indicators for Decision Making
- Please visit the Earthdata Forum, where you can interact with other users and NASA subject matter experts on a variety of Earth science research and applications topics
- Data Visualizations
- GIS-Ready Tools, Tutorials, and Data
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- NASA GIS Tutorials and How-Tos
- For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
- Data Access Tools
- Programming Tools