Agricultural and Water Resources Data Pathfinder
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.
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
|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.
Land Data Assimilation System (LDAS)
Land surface temperature, runoff, soil moisture
Monthly, daily, hourly
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.
- Investigating a Derecho’s Devastating Effects
- Protecting Farmers' Livelihoods Using Satellite Imagery
- NASA Addressing Global Challenges: Food Security
- Transforming Water Management in the U.S. West with NASA Data
- Derecho Flattens Iowa Corn
- A Third of the U.S. Faces Drought
- When Rivers are Borders
Other NASA Assets of Interest
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.
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 (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 (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.
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.
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.
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) 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.
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.
Page Last Updated: Nov 30, 2020 at 11:12 AM EST