1. Learn
  2. Data Pathfinders
  3. Agricultural and Water Resources Data Pathfinder

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 the 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, 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).

About the Data

About the Data

A combination of ground- and satellite-based data 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.

Satellite

Sensor

Spatial Resolution

Temporal Resolution

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

Soil Water Equivalent: 25 km

Precipitation rate: daily

SWE: daily, 5-day, monthly

Aqua

Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E)

(Data only available through 2011)

25 km

daily, 5-day, monthly

Terra and Aqua

Moderate Resolution Imaging Spectroradiometer (MODIS) *

250 m, 500 m, 1 km

1-2 days

Gravity Recovery and Climate Experiment (GRACE)

0.125°

Giovanni: daily

Earthdata: 7-day

Landsat 8

Operational Land Imager (OLI)

15, 30, 60 m

16 days

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

Soil Moisture Active Passive (SMAP)

Radar (active) - no longer functional

Microwave radiometer (passive)

10-40 km

3 days

Shuttle Radar Topography Mission (SRTM) 30 m

Integrated multi-satellite data

Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Algorithm (TMPA)

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

Joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP)

Visible Infrared Imaging Radiometer Suite (VIIRS) *

325-750 m

1-2 days

* sensors from which select datasets are available in LANCE

In addition to mission data, NASA has a series of models that use remotely-sensed 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

Use the Data

Use the Data

Scientists, researchers, land managers, decision makers, and others use remotely-sensed data in numerous ways (to see data use stories, visit the 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.

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

Other NASA Assets of Interest

Other NASA Assets of Interest

The NASA Socioeconomic Data and Applications Center (SEDAC) also has information which may be useful, such as:

  • population density,
  • reservoirs and dams,
  • agricultural land coverage/acreage/area,
  • drought frequency and distribution,
  • economic risk,
  • mortality risk,
  • flood frequency and distribution,
  • nitrogen and phosphorus fertilizer application.

These datasets are available as GeoTIFFs or ESRI Grid Files. Some of these are also available in 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 (AρρEEARS).

The 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 AρρEEARS. In addition to the user interface, AρρEEARS also provides a publicly accessible API.

The 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.

The Short-term Prediction Research and Transition Center (SPoRT) is a NASA 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 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.

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 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.

External Resources

External Resources

There are several tools that consolidate a lot of this information at the U.S. national level 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.
  • GEOGLAM (Group on Earth Observations Global Agricultural Monitoring) 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.

Benefits and Limitations of Remotely-sensed Data

Benefits and Limitations of Remotely-sensed Data

In determining whether or not to use remotely-sensed 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.

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.
  • 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).

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.

Last Updated: Dec 5, 2019 at 11:57 AM EST