Agriculture and Water Management Data Pathfinder - Find Data

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

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:

• 4-Week ESI
• 12-Week ESI

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

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

• Evaporation and Transpiration
• Landsat Data Help Water-Resource Managers
• Landsat: Farming Data From Space

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:

• Landsat Provisional Actual Evapotranspiration (ETa)

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:

• Terra and Aqua MODIS ET using Earthdata Search
• Terra and Aqua MODIS ET from LP DAAC

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):

• GLDAS
• NLDAS
• FLDAS 

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

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

• Agriculture Yield Projections
• Landsat: Farming Data From Space
• Landsat Croplands Data Overview
• NASA and Agriculture: From Seeds to Satellites
• Keeping Track of Food Production From Space

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)

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:

• MODIS Land Cover Type in NASA Worldview
• MODIS Land Cover Type from 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:

• GFSAD30 from Earthdata Search
• GHISACONUS from Earthdata Search
• GHISACASIA from 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

Use the Data

Use Cases and Articles

• New, Gridded Level 3 Data Product Facilitates the Use of GEDI Mission Data
• Earth in the Third Dimension: First GEDI Data Available

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

• MISR Level 3 Data Browser

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 Notebooks
  • Getting Started with GEDI L1B, L2A, and L2B Version 2 Data in Python Tutorial Series
• 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

• MISR LAI 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 in Worldview

MODIS LAI Level 4 data are available starting in 2000 through Earthdata Search:

• MODIS LAI Level 4 data  through Earthdata Search

VIIRS

VIIRS LAI Level 4 data are available starting in 2012 through Earthdata Search:

• VIIRS LAI Level 4 data 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 LAI data in Giovanni

MERRA-2 data are available for download in Earthdata Search:

• LAI 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 

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

• Landsat Croplands Data Overview

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:

• MODIS Surface Reflectance data in 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:

• True Color - Surface Reflectance (MODIS / Terra) - Overview (arcgis.com)

Research quality MODIS data products can be accessed directly from Earthdata Search

• MODIS Surface Reflectance data 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:

• NRT MODIS Surface Reflectance Data from LANCE

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.

• Landsat 8 and 9 OLI in NASA Worldview

Research quality surface reflectance data products can be accessed using USGS EarthExplorer:

• Landsat 7 ETM+
• Landsat 8 OLI
• Landsat 9 OLI-2

HLS

Image

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 LST data in 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:

• ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance Version 3.1 (AST_L1T) data products

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

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:
  • A Global view of Normalized Difference Vegetation Index (NDVI) Anomaly in crop-growing regions from 2000 to 2021 
  • Normalized Difference Vegetation Index (NDVI) Anomaly in crop-growing regions for selected years 

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:

• Vegetation - Surface Reflectance (MODIS / Terra) - Overview (arcgis.com)

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):

• MODIS Vegetation Indices
• VIIRS Vegetation Indices

NRT MODIS vegetation data can be accessed and downloaded through LANCE.

• Daily Rolling 8-Day Vegetation Indices - 250m and 500m

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

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

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

• Water Resource Management Using NASA Earth Science Data
• Chesapeake Bay Agriculture

Data Visualizations

• Connect the Drops with NASA Data

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:

• Total Runoff data 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:

• Total Runoff data 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.

Use the Data

Tutorials

• Let It Snow! Accessing and Analyzing Snow Data at NSIDC DAAC
• Discovering and Differentiating Data with NSIDC Search 
• NetCD-what? An Ecologist’s Guide to Working with Daymet and other NetCDF-formatted Data 

Data Visualizations

• NASA Investigates Water Supply in Snow
• Connect the Drops with NASA Data

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

• Snow Depth and Snow Cover Data Tutorial
• NASA Earthdata Application Programming Interfaces (APIs)
• Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox

Earth Observation Data by Sensor

MODIS

Near real-time snow cover data can be interactively explored using NASA Worldview:

• MODIS Snow Cover in Worldview

Image

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.):

• Water States - Corrected Reflectance (MODIS / Terra) - Overview (arcgis.com)

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:

• AMSR2 SWE in 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.

Image

• ASO Snow Depth and SWE from Earthdata Search

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
  • NLDAS (North American), 0.125° from 1975 to current at monthly and hourly resolutions
  • GLDAS (Global), 0.25° and 1° from 1948 to 2022 at monthly, daily, and 3-hourly temporal resolutions
  • FLDAS, 0.1° and 0.01° from about 1982 - current in daily and monthly temporal resolutions
• Snow Water Equivalent
  • NLDAS (North American), 0.125° from 1975 to present at monthly and hourly resolutions
  • GLDAS (Global), 0.25° and 1° from 1948 to 2022 at monthly, daily, and 3-hourly temporal resolutions

GLDAS model data are available from NASA's GES DISC:

• Snow and Snow Water Equivalent

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:

• Snow depth data 
• Snow water equivalent data

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.

Use the Data

Use Cases and Articles

• Illinois Drought Monitoring using NASA Earth Observations
• Modeling Soil Drydown Parameters for Drought Mitigation in Cropland and Rangeland of Kansas Using NASA Earth Observations
• Tracking Deluge and Drought through Soil Moisture
• Western Soils and Plants are Parched

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: 
    • LIS Primer 
    • LIS Applications

Data Visualizations

• Southern Africa Drought
• Snacktime with NASA: Cheese Board
• Connect the Drops with NASA Data

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:

• SMAP Soil Moisture Data

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:

• SMAP NRT Data

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:

• AMSR-2 Surface Soil Moisture (offered in Day and Night)

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:

• AMSR-2 volumetric soil moisture data in 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:

• AMSR-2 Surface Soil Moisture data

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.

• NLDAS Soil Moisture Data in Worldview
• GLDAS Soil Moisture Data in Worldview

The NLDAS experimental drought monitor is derived from near real-time soil moisture output model data: 
 

• NLDAS Drought Monitor Soil Moisture

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:

• SMERGE in Earthdata Search

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:

• NLDAS (North American) Soil Moisture
• GLDAS (Global) Soil Moisture
• SMERGE Root Zone Soil Moisture 

LDAS Soil Moisture data from GLDAS, NLDAS and FLDAS are available in Earthdata Search:

• LDAS components Soil Moisture data

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):

• Groundwater and Soil Moisture Conditions from GRACE and GRACE-FO Data Assimilation through Earthdata Search

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:

• GEOS-5 SMAP soil moisture data in Worldview

These data products are available from Earthdata Search:

• GEOS-5 SMAP soil moisture data in 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 Giovanni

MERRA-2 Soil Moisture data in Earthdata Search:

• MERRA-2 Soil Moisture

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

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

• Life of the Monsoon
• Connect the Drops with NASA Data

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:

• Terra MODIS Reservoirs
• Aqua MODIS Reservoirs

GIS layer displaying water states (ice, snow, water, etc.):

• Water States - Corrected Reflectance (MODIS/Terra) - Overview (arcgis.com)

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:

• Dams, v1.01: Global Reservoir and Dam (GRanD), v1

For more information on additional data resources on water resources, visit the Sea Level Change Data Pathfinder.

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

Use the Data

Tutorials

• Data Access and Visualization of Model Data at the NASA GES DISC
• GES DISC humidity tutorials and data recipes

Data Visualizations

• Increasingly Dangerous Climate for Agricultural Workers
• How Does NASA Model Atmospheric Patterns?

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:

• AIRS Surface Relative Humidity in 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:

• AIRS Relative Humidity in Giovanni

Research-quality data products can be accessed using Earthdata Search:

• AIRS Relative Humidity in 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:

• MERRA-2 Humidity data in 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:

• MERRA-2 Humidity in 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. 

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

• IMERG Daily Climatology
• IMERG Monthly Climatology
• Precipitation Accumulation and Anomalies
• Life of the Monsoon
• Connect the Drops with NASA Data
• A Map of Freshwater
• How Does NASA Model Atmospheric Patterns?

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

Image

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:

• AMSR-2 Surface Precipitation Rate

Research-quality data products can be accessed using Earthdata Search:

• AMSR-2 Surface Precipitation

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.

• NRT AMSR-2 Surface Precipitation Rate in Earthdata Search 

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:

• IMERG Precipitation Rate
• GPM Precipitation Rate

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:

• IMERG
• GPM
• TMPA

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

Image

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:

• GLDAS Precipitation Total
• NLDAS Precipitation Total

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:

• MERRA-2 Precipitation data in Worldview

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:

• MERRA-2 Precipitation data in Giovanni

Research-quality air surface temperature data products can be accessed using Earthdata Search:

• MERRA-2 Precipitation data in 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.

• POWER provides precipitation data

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

Use the Data

Tutorials

• Data Access and Visualization of Model Data at the NASA GES DISC

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
  • Jupyter Notebooks 
  • Python
• 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

Image

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:

• AIRS Surface Air Temperature in Worldview

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:

• AIRS Surface Air Temperature in Giovanni

Research-quality air surface temperature data products can be accessed using Earthdata Search:

• AIRS Surface Air Temperature in 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:

• GLDAS Near Surface Air Temperatures 
• NLDAS Air Temperatures (2 m above ground)

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:

• GLDAS Near Surface Air Temperatures 
• NLDAS Air Temperatures (2 m above ground)

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. 

Image

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:

• FLDAS Air Temperatures in 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:

• MERRA-2 2-meter Air Temperature in Worldview

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using Giovanni:

• MERRA-2 Temperature in Giovanni

Research-quality air surface temperature data products can be accessed using Earthdata Search:

• MERRA-2 Temperature in 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).

Image

Research-quality air surface temperature data products can be accessed using Earthdata Search:

• Daymet Air Temperature in Earthdata Search