Agricultural and Water Resources Data Pathfinder

This data pathfinder links to NASA Earth observations that help address issues like water management for irrigation, crop-type identification and land use, and drought preparedness.
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, including 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 (SDGs). In addition, there is a SDG 2 Zero Hunger Data Pathfinder for more information on meeting SDG targets.

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.

Please visit the Earthdata Forum, where you can interact with other users and NASA subject matter experts on a variety of Earth science research and applications topics.

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 the European Space Agency (ESA), 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 events. Satellite and ground-based measurements help scientists, researchers, and decision makers in forecasting events and assessing conditions in near real-time in order to make timely decisions. 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 within 3 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 from 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) - no longer functional
Microwave radiometer (passive)
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/td>
* 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 then uses those 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

Scientists, researchers, land managers, decision makers, and others use remote sensing data in numerous ways (to see data use stories, visit NASA's Land Processes Distributed Active Archive Center (LP DAAC) Data in Action page, 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. Image credit: NASA HARVEST

Use Cases:

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite

Earthdata Search

Earthdata Search is NASA's EOSDIS tool for searching and discovering data in the EOSDIS collection as well as U.S and international agencies across the Earth science disciplines.

Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections.


In the project area, for some datasets, users can customize granules. Users can reformat the data and output as HDF, NetCDF, ASCII, KML, or a GeoTIFF and can choose from a variety of projection options. Data can be subset to obtain only the bands that are needed.



HDF and NetCDF files can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.


Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are the more popular ones:

  • Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  • Map animations are a means to observe spatial patterns and detect unusual events over time.
  • Area-averaged time series are used to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step.
  • Histogram plots are used to display the distribution of values of a data variable in a selected region and time interval.

For more detailed tutorials:


NASA's Worldview visualization application visualization application provides the capability to interactively browse over 1000 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview also includes nine geostationary imagery layers from GOES-East, GOES-West, and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables visualization of specific air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.



AppEEARS at LP DAAC offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons.

Performing Area Extractions

After requesting an area extraction, users are taken to the Extract Area Sample page where they specify a series of parameters that are used to extract data for the areas of interest.

Spatial Subsetting

Define the region of interest in one of three ways:

  • Upload a vector polygon file in shapefile format (a single file with multiple features or multipart single features can be uploaded). The .shp, .shx, .dbf, or .prj files must be zipped into a file folder to upload.
  • Upload a vector polygon file in GeoJSON format (users can upload a single file with multiple features or multipart single features).
  • Draw a polygon on the map by clicking on the Bounding box or Polygon icons (single feature only).

Select the date range for the time period of interest.

Specify the range of dates for which data are desired for extraction by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting dates a start and end date in the calendar.

Adding Data Layers

Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching the query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.


Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF4 is selected, outputs will be grouped into .nc files by product and by feature.


Interacting with Results

From the Explore Requests page, click the View icon to view and interact with results. This will take users to the View Area Sample page.

The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AppEEARS ID (AID). If the feature contains attribute table information, users can view the feature attribute table data by clicking on the Information icon to the right of the Feature dropdown. To view statistics from different features or layers, select a different aid from the Feature dropdown and/or a different layer of interest from the Layer dropdown.


Please see the AppEEARS documentation to learn more about downloading the output as GeoTIFF or NetCDF4 files.

Soil Moisture Visualizer

ORNL DAAC developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of different soil moisture datasets over North America. The visualization tool 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.

To use the visualizer, select a dataset of interest under Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and relative time frames of data collection. Upon selecting a parameter, the tool displays a time series with available datasets. All measurements are volumetric soil moisture. Surface soil moisture is the daily average of measurements at 0-5 cm depth and root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth. The visualizer also provides data sources for download.

The Soil Moisture Visualizer allows users to compare soil moisture measurements from multiple sources at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data acquired by NASA’s Soil Moisture Active Passive (SMAP) satellite are shown with data from in situ sensors across the 9-kilometer Equal-Area Scalable Earth (EASE) grid cell encompassing the Tonzi Ranch Fluxnet site in the Sierra Nevada foothills of California, USA. Daily precipitation values for the site (purple spikes) are also provided for reference. Image: NASA ORNL DAAC.

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several tools for subsetting data from the MODIS and VIIRS instruments:

  • With the Global Subset Tool, users can request a subset for any location on Earth that are as GeoTiff and in text format, including interactive time-series plots and more. Users specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, users can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. You will need to register for an Earthdata Login to request data.
  • With the Fixed Sites Subsets Tool, users can download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as National Ecological Observatory Network, Forest Global Earth Observatory network, Phenology Camera network, and Long Term Ecological Research Network.
  • With the Web Service, users can retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into a workflow.

Top image: The Global Subsets Tool enables users to download available products for any location on Earth. Bottom image: The Fixed Sites Subsets Tool provides spatial subsets for established field sites for site characterization and validation of models and remote sensing products. Image: NASA ORNL DAAC.

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Other Resources

NASA Resources

NASA's Socioeconomic Data and Applications Center (SEDAC) also has information which may be useful, 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.

The University of Maryland worked together with NASA and USDA has revamped the Global Agriculture Monitoring (GLAM) Project, a near-real-time monitoring of global croplands, enabling users across the globe to track crop conditions as growing seasons unfolded. Users can access the publicly-available web-interface from anywhere in the world, as all processing takes place on “the cloud” and does not rely on local bandwidth to compile datasets. Other new functions, such as custom time series charts, cropland and crop types masks, etc. have also 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 HARVEST is a multidisciplinary Consortium commissioned by NASA and led by the University of Maryland to enhance the use of satellite data in decision making related to food security and agriculture domestically and globally.

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 United States 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 NASA data in the hands of farmers, water managers and conservation groups to speed up improvements and bring about innovation in water management across 17 western states. It uses publicly-available data and open-source models to deliver satellite-based information on evapotranspiration (the "ET" in OpenET) in areas as small as a quarter of an acre and at daily, monthly and yearly intervals. It is scheduled to launch in 2021.

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 the Earth Science Data Systems 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, launching fall 2021. SWOT will be able to measure lake height and surface area simultaneously allowing for global measurements of lake water storage.

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 (aka "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.

External Resources

There are several tools that consolidate a lot of this information at the U.S. national level and at the global level.

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 United States.

  • 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 data. Define one or more filter criteria, and identify countries (or areas) that meet each and all of the criteria, and their respective 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 (criterion 1), and the GDP per capita is below USD $20,000 (criterion 2).

  • 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.

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 United States 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 United States. 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 3-5 hours after observation, allowing for a faster response.

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, which in turn requires more time between observations of a given area resulting 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.

  • Temporal resolution: Many satellites only pass over the same spot on Earth every 1-2 days and sometimes as seldom as every 16+ days.
  • Spectral Resolution: Passive instruments (those that use energy being 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).

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 SMAP measurements, the spatial resolution is too coarse for farm field-level assessments. This is not the case for instruments at higher resolutions, like those on Landsat.

Find the Data

Land is a key component of the overall Earth system. Changes in the land surface can impact climate, terrestrial ecosystems, and hydrology, which is how water moves on land.
Vegetation plays a critical role in the movement of water at all levels, including the ecosystem and landscape levels.
Water availability is critical for human consumption, agriculture and food security, industry and energy development.
Last Updated
Jul 15, 2021