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Agricultural and Water Resources Data Pathfinder

New to using NASA Earth science data? This pathfinder is designed to help guide you through the process of selecting and using applicable datasets, with guidance on resolutions and direct links to the data sources.

After getting started here, there are numerous NASA resources that can help develop your skills further. If you are new to remote sensing, check out What is Remote Sensing? or view NASA's Applied Remote Sensing Training on Fundamentals of Remote Sensing.

The severity of California's 2014 drought is illustrated in these images of Folsom Lake, a reservoir in Northern California. NASA and California are collaborating to use NASA Earth observation assets to help the state better manage its water resources and monitor and respond to the ongoing drought.

The severity of California's 2014 drought is illustrated in these images of Folsom Lake, a reservoir in Northern California. NASA and California are collaborating to use NASA Earth observation assets to help the state better manage its water resources and monitor and respond to the ongoing drought. Image Credit: California Department of Water Resources

The economic impacts associated with compromised water availability and food production due to flooding, severe storms, and drought are devastating for countries. Drought, in fact, ranks as one of the top weather-related disasters, following severe storms and inland flooding. As such, it is critical for water resource managers and agricultural decision makers to monitor water availability and drought conditions.

When forecasting future events or responding to current events, there are three primary areas of focus: land, water, and vegetation. On Earth's land surface we can observe reflectance, temperature, elevation, and possible runoff. With water, we can look at precipitation, snow water equivalent, groundwater, and soil moisture, whether from a water availability standpoint or for the assessment of irrigation strategies. With vegetation, we can assess ecosystem health and phenology through vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), characterize vegetation structure through variables such as Leaf Area Index (LAI), and monitor how plants use water via evapotranspiration (ET). This pathfinder is divided into these three primary sections, providing measurements to help in making agricultural and water management decisions, as well as information to assess Sustainable Development Goals.

About the Data

About the Data

NASA collaborates with other federal entities and international space organizations, including NOAA, USGS, the Japan Aerospace Exploration Agency (JAXA) and Ministry of Economy, Trade, and Industry (METI), and ESA (the European Space Agency), to provide a combination of ground- and satellite-based data that provides a unique view of the globe to better understand the impacts of climate change. Satellite and ground-based measurements help scientists, researchers, and decision makers in forecasting events and assessing conditions in near real-time. NASA, in collaboration with other organizations, has a series of instruments that provide information for understanding a number of phenomena associated with water availability and crop yield. NASA's Earth science data products are validated, meaning the accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.

Datasets referenced in this pathfinder are from sensors shown in the table below, with their spatial and temporal resolutions. NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides select datasets to the public generally within three hours of satellite observation, which allows for near real-time (NRT) monitoring and decision making.

Note: This is not an exhaustive list of datasets but rather only includes datasets available through NASA's Earth Observing System Data and Information System (EOSDIS).

Measurement Satellite Sensor Spatial Resolution Temporal Resolution
Elevation Shuttle Radar Topography Mission (SRTM) 30 m
Evaporative Stress Index, Evapotranspiration International Space Station ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) 70 x 70 m, 30 x 30 m Target areas every 1-7 days
Evapotranspiration, Land Cover Type, Land Surface Temperature, Snow Cover, Surface Reflectance, Vegetation Indices Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) * 250 m, 500 m, 1 km 1-2 days
Groundwater Gravity Recovery and Climate Experiment (GRACE) 0.125° Giovanni: daily

Earthdata: 7-day
Land Surface Temperature, Snow Cover, Surface Reflectance, Vegetation Indices Joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) * 325-750 m 1-2 days
Land Surface Temperature, Surface Reflectance Landsat 8 Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
15, 30, 60 m 16 days
Precipitation Integrated multi-satellite data Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Algorithm (TMPA) and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) 0.1° x 0.1° or 0.25° x 0.25° half hourly, daily, monthly
Precipitation, Snow Water Equivalent (SWE) Japanese Aerospace Exploration Agency Global Change Observation Mission -Water Satellite 1 ("Shizuku"), (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) * Precipitation Rate: imagery resolution is 2 km, sensor resolution is 5 km

SWE: 25 km
Precipitation rate: daily

SWE: daily, 5-day, monthly
Snow Water Equivalent Aqua Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E)
(Data only available through 2011)
25 km daily, 5-day, monthly
Soil Moisture Soil Moisture Active Passive (SMAP) Radar (active sensor; no longer functional)

Microwave radiometer (passive sensor)
10-40 km 3 days
Surface Kinetic Temperature, Surface Reflectance, Topography Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 15 m Very Near Infrared (VNIR), 30 m short-wave infrared (SWIR), 90 m thermal infrared (TIR) Variable
* sensors from which select datasets are available in LANCE

In addition to mission data, NASA has a series of models that use remote sensing data as inputs to obtain more complex data parameters. The Land Data Assimilation System (LDAS) provides data within 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 runoff and evapotranspiration.

Model Source

Data Parameter

Spatial Resolution

Temporal Resolution

Land Data Assimilation System (LDAS)

Land surface temperature, runoff, soil moisture

GLDAS: 0.25°

FLDAS: 0.1°

NLDAS: 0.125°

Monthly, daily, hourly

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Use the Data

Use the Data

Scientists, researchers, land managers, and decision makers use remote sensing data in numerous ways (to see data use stories, see the Data in Action Page at NASA's Land Processes Distributed Active Archive Center [LP DAAC] or read the Data User Profiles and Freshwater Feature Articles on Earthdata). Satellite imagery coupled with ground-based data aids in water allocation, agricultural monitoring, irrigation management, flood and drought management, reservoir and dam management, and food security. NASA Earth science observations are transforming our approach to some of these critical issues.

Earth observations can be used in addressing critical issues in food security from risk assessments to monitoring interventions.

Earth observations can be used in addressing critical issues in food security from risk assessments to monitoring interventions. Image credit: NASA HARVEST

Use Cases:

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Other NASA Assets of Interest

Other NASA Assets of Interest

NASA's Socioeconomic Data and Applications Center (SEDAC) has information that may be useful in studies related to agricultural and water resources, such as:

  • global agricultural inputs/pesticide grids
  • population density
  • reservoirs and dams
  • agricultural land coverage/acreage/area
  • drought frequency and distribution
  • economic risk
  • mortality risk
  • flood frequency and distribution
  • food insecurity hotspots
  • agricultural pesticide use (Pesticide Dataset Announcement)
  • nitrogen and phosphorus fertilizer application

These datasets are available as GeoTIFFs or ESRI Grid Files. Some of these are also available in NASA Worldview. SEDAC's Gridded Population of the World (GPW) Version 4 is also available via the LP DAAC's Application for Extracting and Exploring Analysis Ready Samples (AppEEARS).

NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) provides access to and customized visualizations of various environmental data through the Spatial Data Access Tool (SDAT). Resources are available for learning how to access, process, and manage data from ORNL DAAC.

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.

NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) optimally reorganized some large hydrological datasets as time series (also known as data rods) for a set of water cycle-related variables from the NLDAS and GLDAS, the Land Parameter Parameter Model (LPRM), TRMM, and GRACE data assimilation. These are available at GES DISC Hydrology Data Rods. GES DISC also provides several other model soil moisture datasets: the LPRM product and the SoilMERGE (SMERGE) product. Spaceborne observed brightness temperatures are converted, using LPRM, to soil moisture, and the SMERGE product combines long-term (January 1979–May 2019) satellite-based soil moisture retrievals with land surface model estimates acquired from Phase 2 of the NLDAS to produce a 0.125-degree, daily, root-zone soil moisture product within the conterminous United States.

The University of Maryland worked with NASA and the USDA to create the original Global Inventory Modeling and Mapping Studies (GIMMS) Global Agriculture Monitoring System. Recognizing the emergence of new needs for agricultural monitoring along with better technology and computing power, the Global Agriculture Monitoring system 2 (GLAM 2) was developed by NASA Harvest. GLAM 2 is a near-real-time monitoring of global croplands that enables global users to track crop conditions as growing seasons unfolded. 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, recently have been implemented. 

NASA's Short-term Prediction Research and Transition Center (SPoRT) is a project to transition unique observations and research capabilities to the operational weather community to improve short-term forecasts on a regional scale. The SPoRT site provides access to real-time data from a variety of missions, as well as evaluation- and research-based modeling products.

NASA's Applied Sciences Food Security & Agriculture Program promotes the use of Earth observations to strengthen food security, support market stability and protect human livelihoods. Together with partners in the United States and around the world, they help bolster food security, improve agricultural resilience and reduce price volatility for vulnerable communities. NASA’s Harvest Program operates as a consortium made up of over 40 global partners, working to enable and advance adoption of satellite Earth observations by public and private organizations to benefit food security, agriculture, and human and environmental resiliency in the U.S. and worldwide.

NASA's Applied Sciences Water Resources Program helps discover, develop, and demonstrate new practical uses for NASA's Earth observations in the water resources management community. They work with a wide range of partners in the U.S. and around the world to find innovative solutions as shifts in land use, changing climates and growing populations stress water supplies.

OpenET is a new web-based platform that puts openly-available evapotranspiration (ET) data in the hands of farmers, water managers, and conservation groups to speed up improvements and bring about innovation in water management across 17 states in the Western U.S. The project is being led by NASA, the Desert Research Institute (DRI), and the Environmental Defense Fund (EDF), with in-kind support from Google Earth Engine. 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 and at daily, monthly, and yearly intervals.

Map of the world showing locations of LOCSS study sites current from June 2019, projecting to 2020.

LOCSS locations as of June 2019 and planned sites projected through 2020.

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).

Lake Observations by Citizen Scientists and Satellites (LOCSS) is a citizen science program funded by NASA's Earth Science Data Systems (ESDS) Program to better understand how the water volume in lakes is changing. Citizen scientists report lake height by reading simple lake gauges. The data collected will be used to provide a foundation for the upcoming Surface Water and Ocean Topography (SWOT) mission (scheduled for launch in 2022). SWOT will be able to measure lake height and surface area simultaneously allowing for global measurements of lake water storage.

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External Resources

External Resources

Several tools are available that consolidate agricultural and water resource information at the U.S. national and at the global level.

Famine Early Warning System Network (FEWS NET)

Map of near-term acute food insecurity in Africa during the month of September 2019 from the Famine Early Warning System Network.

  • Famine Early Warning System Network (FEWS NET) provides early warning and analysis on acute food insecurity. Analysts and specialists in 22 field offices work with U.S. government science agencies, national government ministries, international agencies, and NGOs to produce forward-looking reports on more than 36 of the world's most food-insecure countries.
  • Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) incorporates NDVI, temperature, precipitation, soil moisture, ET, and runoff data to determine crop conditions for a variety of different crops in Early Warning countries (Africa and Asia) and Agricultural Market Information System (AMIS) countries (North America, Europe, and Asia). The Crop Monitor Exploring Tool provides all of this information in an online interactive tool.
  • NOAA's National Integrated Drought Integration System (NIDIS) provides drought-related information and resources and also has a suite of data, maps, and tools for exploring drought across the U.S.
  • Data Rods Explorer (DRE) is a web client app that enables users to browse several NASA-hosted datasets. The interface enables the visualization and downloading of NASA observation retrievals (parameters have been retrieved from the raw data through a series of steps) 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.
  • United Nations Water Data Lab allows for multi-criteria analysis of the Sustainable Development Goal 6 (Clean Water and Sanitation) data. Users can define one or more filter criteria and identify countries (or areas) meeting the selected criteria along with information about population and land area. For example, you can identify countries in Asia where at least 80% of the population has access to safe drinking water and the gross domestic product (GDP) per capita is below USD $20,000.
  • Climate Engine Drought Severity Evaluation Tool allows you to look at drought-related datasets either through map layers or time series figures.
  • European Drought Observatory provides drought-related information across Europe. The site contains data-based maps of indicators, tools for visualizing and analyzing the information, and reports of specific regional droughts.

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Benefits and Limitations of Remote Sensing Data

Benefits and Limitations of Remote Sensing Data

In determining whether or not to use remote sensing data, it is important to understand not only the benefits but also the limitations of the data. Benefits of using satellite data include:

  • Filling in data gaps: the U.S. is fortunate to have numerous ground-based measurements for assessing water storage, precipitation, and more. However, this is not the case in other countries and even in some of the more remote areas of the U.S. Satellite data provide local, regional, and global spatial coverage and also are useful for observing areas that are inaccessible.
  • Monitoring in near real-time: some satellite information is available three to five hours after observation, allowing for a faster response.

With satellite data, assessments can be made regarding the land surface, runoff, irrigation needs, and crop health. Incorporating satellite data with in-situ data into modeling programs makes for a more robust and integrated forecasting system.

There are limitations specific to using satellite data in water availability and agricultural assessments.

  • Spatial resolution: While lower resolution data provide a more global view, often, as with measurements from NASA's Soil Moisture Active Passive (SMAP) satellite, the spatial resolution is too coarse for farm field-level assessments. This is not the case for instruments that collect data at higher resolutions, like those aboard the joint NASA/USGS Landsat series of satellites.
  • Temporal resolution: Many satellites only pass over the same spot on Earth every one to two days and sometimes as seldom as every 16 days or more.
  • Spectral Resolution: Passive instruments (those that use energy reflected or emitted from Earth for measurements) are not able to penetrate cloud or vegetation cover, which can lead to data gaps or a decrease in data utility. This is not the case when using data from microwave or thermal sensors (active sensors).

It is difficult to combine all of the desirable features into one remote sensor. To acquire observations with high spatial resolution (like Landsat), a narrower swath is required. This, in turn, requires more time between observations of a given area and results in a lower temporal resolution. Researchers have to make trade-offs. Finding a sensor with the spatio-temporal resolution capable of addressing your research, application, or decision making process needs is a crucial first step to getting started with using remote sensing data.

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Published May 21, 2019

Page Last Updated: Jul 30, 2021 at 3:42 PM EDT