Agricultural and Water Resources Data Pathfinder - Find Data

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.

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. The land surface, including land cover types, land surface temperature, and topography, are critical to monitoring agricultural practices and water resource availability and providing interventions when necessary.

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Farms rise from a floodplain where the Mojave nation meets Arizona, Nevada, and California.

Land 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 with continued observations using sustained land imaging and multi-spectral high-resolution imagery.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument aboard NASA's Terra satellite is a high-resolution instrument that acquires visible and near-infrared (VNIR) reflectance data at 15 m spatial resolution and short wave infrared (SWIR) reflectance data at 30 m spatial resolution. A cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), ASTER data are distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC). 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 surface reflectance products are processed on-demand and can be requested through Earthdata Search (note that there is a limit of 2,000 granules per order):

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September 10, 2009, Landsat image of farmland across northwest Minnesota. Image: NASA Earth Observatory.

The Enhanced Thematic Mapper (ETM+) sensor and the Operational Land Imager (OLI) instruments aboard the joint NASA/USGS Landsat 7 (ETM+) spacecraft and Landsat 8 (OLI) spacecraft acquire VNIR data at 30 m spatial resolution every 16 days (less days as you move away from the equator). Landsat 9 is scheduled for launch in September 2021 and carries two instruments: the OLI-2 (which is a copy of the Landsat 8 OLI) and the Thermal Infrared Sensor-2 (TIRS-2, which measures land surface temperature in two thermal infrared bands). Landsat satellite operations and data archiving are coordinated at the USGS Earth Resources Observation and Science (EROS) Center, which also is the location of LP DAAC.

Research quality land surface reflectance data products can be accessed directly using Earthdata Search (note: you will need a USGS Earth Explorer login—which is separate from the NASA Earthdata Login—to download Landsat data):

Another high-resolution land surface reflectance imagery option is Harmonized Landsat and Sentinel-2 (HLS) imagery. HLS imagery provides consistent surface reflectance and top of atmosphere brightness data from the Landsat 8 OLI 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 data are currently provisional and not considered standard data products. A science quality HLS dataset is expected to be released in fall 2021.

The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) tool, available through LP DAAC, offers a simple and efficient way to access, transform, and visualize geospatial data from a variety of federal data archives, including the USGS Landsat Analysis Ready Data (ARD) surface reflectance product. Landsat ARD are for Landsat Collection 1 and are available for the conterminous United States, Alaska, and Hawaii using Landsat 8 OLI/Thermal Infrared Sensor (OLI/TIRS), Landsat 7 ETM+, and Landsat 4-5 Thematic Mapper (TM) data.

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Satellite images show the differences in land surface temperature during the day (middle image) and at night (bottom image). Top image is a natural color image. Darker colors indicate cooler temperatures. Heavily forested areas remain relatively cool throughout the day while barren and arid areas can be significantly warmer. These images were acquired over the state of Oregon, USA, in the early morning and afternoon of July 6, 2011. Image: NASA Earth Observatory.

Land Surface Temperature (LST) describes processes such as the exchange of energy and water between the land surface and Earth's atmosphere and influences the rate and timing of plant growth. LST data can improve decision-making for water use and irrigation strategies.

Research quality LST data products can be accessed directly from Earthdata Search and also are available through the Data Pool at LP DAAC. Data from the MODIS and ASTER instruments are available in HDF format; data from the VIIRS and ECOSTRESS instruments are available in HDF5 format:

To quickly extract a subset of ECOSTRESS, MODIS, or VIIRS data for a region of interest, use the AppEEARS tool available through LP DAAC or the subsetting tools available through NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC).

LST data can be visualized and interactively explored using NASA Worldview:

LST data also are produced as part of the joint NASA/USGS Landsat series of Earth observing missions:

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An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

Knowing local topography is essential for professionals seeking to assess an area's runoff potential and the availability of water in lower-lying areas.

A method for delineating topography is the Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The ASTER Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover, however. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc-second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

Runoff potential is very 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 using remote sensing. 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
    Select a map plot (you can generate a time-averaged map, an animation, or seasonal maps), date range and region, select your variable and then plot the data. Data can be downloaded as GeoTIFF.
  • GLDAS (Global) Runoff Data in Giovanni
    Select a map plot (you can generate a time-averaged map, an animation, or seasonal maps), date range, select your variable and then plot the data (data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the starting and end date to ensure you access the desired dataset). Data can be downloaded as GeoTIFF.

 

Vegetation is a key component of the overall Earth system. It plays a critical role in the movement of water at all levels, including the ecosystem and landscape levels. Ecosystem and landscape health, including vegetation greenness, land cover type, evapotranspiration, and evaporative stress, are critical to monitoring agricultural practices and water resource availability and providing interventions when necessary.

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NDVI time series of nearly four years of HLS data for three Midwestern U.S. farms. The colored points represent different crop types, including corn, soy, wheat, and cover crops. The red shaded background represents a range of NDVI from time series for nearby fields. For more information on this work, see Using NASA's HLS Product to Give Farmers Real-Time Crop Health Information on the NASA Landsat Science website. Image credit: Landsat/Sulla-Menashem, et al.

Vegetation indices measure the amount of green vegetation over a given area and can be used to assess vegetation health. Commonly used vegetation indices are the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) and EVI2.

The NDVI takes the difference between near-infrared (NIR) and red reflectance divided by their sum. NDVI values range from -1 to 1. Low values of NDVI generally correspond to barren areas of rock, sand, exposed soils, or snow. Higher NDVI values indicate greener vegetation, including forests, croplands, and wetlands. The EVI minimizes canopy-soil variations and improves sensitivity over dense vegetation conditions.

New animations by NASA's Science Visualization Studio show NDVI anomalies over time globally and for selected regions:

Vegetation products created from data acquired by the MODIS instrument aboard NASA's Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite can be accessed in several ways. Research quality surface reflectance data products can be accessed directly using Earthdata Search (datasets are available as in HDF format but are, in some cases, customizable to GeoTIFF):

AppEEARS, available through NASA's Land Processes Distributed Active Archive Center (LP DAAC), offers a simple and effective way to extract, transform, visualize, and download vegetation-related data products produced from data acquired by the MODIS and VIIRS instruments. 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.

LP DAAC's Getting Started with Cloud-Native Harmonized Landsat Sentinel (HLS) Data in Python Jupyter Notebook shows how to extract an EVI Time Series from HLS imagery. In addition, MODIS and VIIRS subsetting tools available through NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) provide a means to simply and efficiently access MODIS and VIIRS vegetation-related data products. See the Tools for Data Access and Visualization section for additional information on both of these tools.

Data products 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. Follow these steps to plot data in Giovanni: 1) Select a visualization type. 2) Select a date range. Data are available in multiple temporal resolutions, so be sure to note the resolution and the start and end dates of datasets to ensure you can analyze the desired data. 3) Select a region of interest using a bounding box, shapefile, or geographic coordinates. 4) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a plot type, region of interest, and data variables, see the Giovanni User Manual.

  • MODIS NDVI in Giovanni
    Select a map plot, date range, and region and plot the data. Data can be downloaded as GeoTIFF.

Near real-time imagery can be interactively explored using NASA Worldview:

  • MODIS NDVI in Worldview
    This dataset has a spatial resolution of 250 m and the temporal resolution is an 8-day product, updated daily. 16-day and monthly data are also available in Worldview.
  • MODIS EVI in Worldview
    This dataset is monthly at 1 km spatial resolution. Rolling 8-day and 16-day data are also available in Worldview.

MODIS NDVI is also available through geospatial web map services. For information on accessing the data within a GIS program, see the Biosphere Geospatial Services section in the Earthdata GIS Data Pathfinder.

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MODIS Land Cover Type as seen in the NASA Worldview data visualization application. Image: NASA Worldview.

Deforestation for agriculture and livestock production contributes to land degradation. Through the SDGs, the U.N. advocates for sustainable land management, which seeks to maintain vegetative cover and health as well as make efficient use of water, nutrients, and pesticides. Land cover is one of the indicators that can help quantify land degradation.

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 Terra and Aqua 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. Land cover types are based on the International Geosphere-Biosphere Programme classification scheme. These data can be accessed and downloaded through Earthdata Search and NASA Worldview:

LP DAAC provides access to three products related to agricultural land cover types:

  • Global Food Security-support Analysis Data 30 meter (GFSAD30) Cropland Extent data product
  • Global Hyperspectral Imaging Spectral-library of Agricultural crops

The GFSAD30 collection 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 are an important resource for policymaking and provide baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.

GHISACONUS provides 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, operational 2000 to 2017). Crop growth states (emergence/very early vegetative, early and mid-vegetative, late vegetative, critical, maturing/senescence, and harvest) for the 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. The GHISA hyperspectral library for the two irrigated areas was developed using EO-1 Hyperion hyperspectral data acquired in 2007 and Analytical Spectral Devices, Inc. (ASD) Spectroradiometer data acquired in 2006 and 2007.

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Colors in this screenshot of the USDA CropScape tool indicate specific crops. Note the high concentration of yellow (corn) in Illinois, Iowa, and Indiana and the bright red indicating cotton in west Texas. Image: USDA CropScape.

The USDA's 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.

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. NASA Harvest operates as a consortium of over 40 global partners that work 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 Harvest GLAM 2 is a near real-time monitoring of global croplands that enables global users to track crop conditions as growing seasons unfold. 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.

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The combination of evaporation from the land surface and transpiration from plants is evapotranspiration, abbreviated ET. This parameter approximates the consumptive use of a landscape’s plants. Image: U.S. Geological Survey.

Evapotranspiration (ET) is the sum of evaporation from land surface and transpiration in vegetation. ET measurements are extremely 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 carbon assimilation through photosynthesis (primary production) and also increases 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 conserve 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.

One of the issues in acquiring ET data is that ET can't be measured directly with satellite instruments as it is dependent on variables including land surface temperature, air temperature, and solar radiation. However, NASA has Level 4 data products that incorporate daily meteorological reanalysis data with remote sensing data to arrive at estimations of ET, such as the MODIS MOD16 product.

Research quality MODIS Level 4 ET products are available in yearly and 8-day temporal resolutions with 500 m pixel size using Earthdata Search:

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ECOSTRESS evapotranspiration daily data from the ECOSTRESS ECO3ETALEXI product over the state of Wisconsin, USA, acquired May 29, 2021. Image: NASA Land Processes DAAC.

NASA's ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) aboard the International Space Station measures the temperature of plants to better understand how they respond to the stress of insufficient water availability. ECOSTRESS was installed in June 2018 and uses a multispectral thermal infrared radiometer to measure radiance, which is converted into surface temperature and emissivity.

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 are, in some cases, customizable to GeoTIFF:

AppEEARS, available through LP DAAC, offers a simple and effective way to extract, transform, visualize, and download MODIS and ECOSTRESS ET data products. 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.

In addition, MODIS and VIIRS subsetting tools available through ORNL DAAC provide a means to simply and efficiently access and visualize MODIS ET data products. 

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 within 2-12% error.

GLDAS data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series through an online interactive data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a plot type. 2) Select a date range. Data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you can analyze the desired data. 3) Select a region of interest using a bounding box, shapefile, or geographic coordinates. 4) Check the box of the variable in the left column that you would like to include and then plot the data. Maps and plots for multiple variables can be generated at the same time. For more information on choosing a type of plot, see the Giovanni User Manual.

  • GLDAS ET in Giovanni
    Data are available with a temporal resolution of 3-hourly, daily, and monthly.

OpenET is a new web-based platform that puts openly-available 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. 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. OpenET was developed through a unique public-private partnership led by NASA, the Desert Research Institute (DRI), and the Environmental Defense Fund (EDF), with in-kind support from Google Earth Engine. The OpenET Team also includes scientists and software engineers from the U.S. Geological Survey, U.S. Department of Agriculture, HabitatSeven, California State University Monterey Bay, University of Idaho, University of Maryland, University of Nebraska-Lincoln, UCLA, and Universidade Federal do Rio Grande do Sul in Brazil.

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Level 4 Evaporative Stress Index data from the ECOSTRESS ECO4ESIPTJPL product showing the Central Valley in California, USA, and acquired on August 5, 2018. High ESI is in shades of green and low ESI in shades of red. Image: NASA Land Processes DAAC.

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.

Level 4 ECOSTRESS ESI and water use efficiency (WUE) products can be accessed using Earthdata Search. The ESI product is derived from the ratio of Level 3 actual ET to potential ET (PET), calculated as part of an algorithm. 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. ESI data can be used to assess agricultural drought and observe vegetation stress.

AppEEARS, available through LP DAAC, offers a simple and effective way to extract, transform, visualize, and download ECOSTRESS Level 1 through Level 4 data products. 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.

 

Water is a key component of the overall Earth system, cycling through each component, moving within the atmosphere, the ocean, the cryosphere (including snow cover and snow pack), surface water of rivers and lakes, and subsurface water. Water availability is critical for human consumption, agriculture and food security, industry and energy development. Assessing water availability, including the amount and type of precipitation, including snow and snow pack, groundwater and soil moisture, is critical to monitoring agricultural practices and water resource availability and providing interventions when necessary.

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This animated GIF created from IMERG data on NASA's Worldview application shows daily precipitation over North America between August 15 and August 30, 2020. Hurricanes Marco and Laura can be seen in the Gulf of Mexico starting August 21. Image: NASA Worldview.

Rain and snow provide the water upon which agriculture depends. This can be direct, through rainfall or snowpack on agricultural fields, or indirect, through water reserves in lakes, reservoirs, and groundwater that are used for irrigation. Understanding how this water is distributed and how it changes is essential to food security and sustainable water usage.

NASA's Precipitation Measurement Missions (PMM) provide a more than 22-year 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.

Data products from TRMM and GPM are available individually and have been integrated with data from a global constellation of satellites of opportunity 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 now 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.

In addition to the precipitation products developed by PMM, NASA's Hydrological Sciences Laboratory, in collaboration with other agencies, has developed land surface models incorporating satellite precipitation estimates with ground-based data. These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses inputs of measurements including 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 has a spatial resolution of 1 degree and 0.25 degrees, with data available for all land north of 60 degrees south latitude. GLDAS data are available from January 1948 to present. NLDAS is currently running operationally in near real-time (with an approximate four-day lag) on a 1/8th-degree grid with an hourly timestep over central North America (between approximately 25 to 53 degrees north latitude and -125 to -67 degrees west longitude). Retrospective hourly/monthly NLDAS datasets are available from January 1979 to present.

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Map of near-term acute food insecurity from July to September 2021 from the Famine Early Warning System Network (FEWS NET). Colors indicate food insecurity levels: Yellow = stressed; Orange = crisis; Red = emergency. Image: FEWS NET.

The Famine Early Warning Systems Network (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-country settings. Adopting 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. The goal of FLDAS is to achieve more effective use of limited available hydroclimatic observations. FLDAS data have a spatial resolution of 0.1 degrees and are available for all land north of 60 degrees south latitude. Daily FLDAS data are available in 15-minute time steps with a data record available starting in January 1981.

Various NASA precipitation products can be visualized as time-averaged maps, animations, seasonally-averaged maps, scatter plots, or time series using an online interactive data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a plot type. 2) Select a date range. Data are available in multiple temporal resolutions, so be sure to note the resolution and the start and end dates of datasets to ensure you can analyze the desired data. 3) Select a region of interest using a bounding box, shapefile, or geographic coordinates. 4) Check the box of the variable in the left column that you would like to include and then plot the data. Maps and plots for multiple variables can be generated at the same time. For more information on choosing a type of plot, see the Giovanni User Manual.

Near real-time data can be visualized and interactively explored using NASA Worldview:

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 at NASA's Land Processes DAAC (LP DAAC). Along with daily data, annual Daymet climatologies also are available.

Billions of people worldwide rely on seasonal water runoff from snowpack and glaciers for irrigation and drinking water. 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.

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Snow Water Equivalent (SWE) over the Tuolumne Basin in Yosemite National Park, CA, USA, on June 4, 2017. Darker blue colors indicate higher SWE values. Image: NASA Airborne Snow Observatory.

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 snow melt and the water availability for regions in lower elevations. The MODIS instrument aboard NASA's Terra satellite measures snow cover. The Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) instrument aboard NASA's Aqua satellite, and the AMSR2 instrument aboard the Japan Aerospace Exploration Agency's Global Change Observation Mission 1st – Water (GCOM-W1) spacecraft, provide SWE data.

Research quality data products can be accessed using Earthdata Search (datasets are available in HDF5 format which can be opened using NASA's Panoply application):

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

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Image showing Airborne Snow Observatory (ASO) lidar coverage of the Kings River basins in central California, USA. This image was acquired during snow surveys of the Tuolumne, Kings, Merced, and Kaweah river basins undertaken April 17-21, 2019. Image: NASA Jet Propulsion Laboratory.

NASA's Airborne Snow Observatory (ASO) mission collects data on the snow melt 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 snow-melt season; these data are available through Earthdata Search. 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.

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. NSIDC is part of the Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder, and the location of NASA's NSIDC DAAC.

Another NASA source for SWE data is Daymet, which can be accessed through 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 AppEEARS application at LP DAAC. Along with daily data, annual Daymet climatologies also are available.

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Screenshot from a NASA Scientific Visualization Studio video created using GRACE data collected between 2002 and 2016 showing global changes in terrestrial water storage over time. Blue colors indicate greater freshwater storage than average. Orange, red, and crimson colors indicate lower freshwater storage than average. View this animation at https://svs.gsfc.nasa.gov/12950. Image: NASA Scientific Visualization Studio.

Water scarcity is a threat to many countries around the world. According to the U.N., water use has been growing globally at twice the rate as the global population is increasing. More and more areas are reaching the limit at which water services can be sustainably delivered, especially in arid regions. Groundwater, a major water resource for maintaining food security, is declining through the extensive use of water for agricultural irrigation, where aquifer recharge cannot keep up with groundwater extraction. Instruments aboard the joint NASA/German Space Agency Gravity Recovery And Climate Experiment (GRACE, operational 2002 to 2017) and GRACE Follow-On (GRACE-FO, launched in 2018) satellites are obtaining measurements about changes in Earth's gravity that can be used to assess changes in water storage. Since water has mass, changes in groundwater storage can be detected as changes in gravity.

Data from GRACE and GRACE-FO are available from 2002 to present; the data track total water storage time-variations and anomalies (changes from the time-mean) at a resolution of approximately 90,000 km2 and larger. These measurements are unimpeded by clouds and track the entire land water column from the surface down to deep aquifers. GRACE and GRACE-FO data are uniquely valuable for regional studies to determine general trends in land water storage as well as for assessing basin-scale water budgets (e.g., the balance between precipitation, evapotranspiration, and runoff).

Note that there are several limitations with GRACE data:

  • The resolution of the data are greater than 150,000km2 so it only measures change within large aquifers;
  • GRACE cannot detect issues of water quality (salt water intrusion, chemicals, etc.);
  • GRACE does not provide information on groundwater flow because the satellite only measures in one dimension, while groundwater flow is not limited to one dimension; and
  • GRACE does not provide information on whether the aquifer is confined or unconfined.

The GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height dataset provides gridded monthly global water storage/height anomalies relative to a time-mean. The data are processed at JPL using the mascon approach. Mass Concentration blocks (mascons) are a form of gravity field basis functions to which GRACE observations are optimally fit. For more information on this approach, see the JPL Monthly Mass Grids webpage. Data are represented as Water Equivalent Thickness (WET), which is a way of representing changes in the gravity field in hydrological units. WET represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (including rivers, lakes, and reservoirs), as well as groundwater and aquifers.

Research-quality data products can be accessed using Earthdata Search. Datasets are available in NetCDF format that can be opened using NASA's Panoply application or imported into a GIS system.

NASA's Physical Oceanography DAAC (PO.DAAC) has developed a Python script to convert the JPL GRACE Mascon file from NetCDF4 to GeoTIFF format. This GRACE Python script decomposes the multi-year monthly mascon files in NetCDF format into single files in GeoTIFF format for each month.

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GRACE-based shallow groundwater drought indicator describing current wet or dry conditions over the continental U.S., for August 02, 2021. Image: NASA GRACE; National Drought Mitigation Center, University of Nebraska-Lincoln.

GRACE and GRACE-FO data can be visualized and interactively explored using NASA Worldview and PO.DAAC's State of the Ocean (SOTO) data visualization tools. Both products incorporate a Coastal Resolution Improvement filter that reduces leakage errors across coastlines.

Scientists at NASA's Goddard Space Flight Center use GRACE-FO data to generate weekly groundwater and soil moisture drought indicators. These are based on terrestrial water storage observations and are integrated with other observations using a sophisticated numerical model of land surface water and energy processes. The drought indicators describe current wet or dry conditions, expressed as a percentile showing the probability of occurrence for a specific location and time of year, with lower values (orange/red) indicating drier than normal conditions and higher values (blues) indicating wetter than normal conditions. The drought model is also used to make forecasts of expected drought conditions one, two, and three months into the future.

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Map based on Soil Moisture Active Passive (SMAP) data showing soil moisture anomalies across the U.S. in mid-May 2018. Soil anomaly data indicate how much the moisture content was above or below the norm. Image: NASA Earth Observatory.

Soil moisture is important for surface hydrology studies as it controls the amount of water that can infiltrate the ground, replenish aquifers, and contribute to excess runoff. In addition, water availability, specifically with regard to soil moisture, is vital for crop growth and yield. Timely seasonal soil moisture information is critical for food security and provides the ability to detect drought and other water-related stressors on crop production.

NASA's Soil Moisture Active Passive satellite (SMAP, launched in 2015) measures the moisture in the top 5 cm of soil globally every 2-3 days at a resolution of 9-36 km. 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 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.

Research quality data products can be accessed using Earthdata Search (datasets are available in HDF5 format which are also customizable to GeoTIFF):

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Soil moisture as visualized using the ORNL DAAC Soil Moisture Visualizer. The map shows a flight path over Arizona in 2013. In the graph, AirMoss rootzone soil moisture data are plotted with SMAP rootzone soil moisture. Root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth. Image: NASA ORNL DAAC.

The Soil Moisture Visualizer—which is available through ORNL DAAC—integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. AppEEARS, available through LP DAAC, offers a simple and effective way to extract, transform, visualize, and download SMAP data products. 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.

NLDAS and GLDAS data products 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. Follow these steps to plot data in Giovanni: 1) Select a plot type. 2) Select a date range. Data are available in multiple temporal resolutions, so be sure to note the resolution and the start and end dates of datasets to ensure you can analyze the desired data. 3) Select a region of interest using a bounding box, shapefile, or geographic coordinates. 4) Check the box of the variable in the left column that you would like to include and then plot the data. Maps and plots for multiple variables can be generated at the same time. For more information on choosing a type of plot, see the Giovanni User Manual.

Near real-time SMAP imagery can be interactively explored using NASA Worldview:

Farmers, researchers, meteorologists, and other stakeholders have access to high-resolution NASA soil moisture data thanks to a tool developed by the USDA's National Agricultural Statistics Service, NASA, and George Mason University. The Crop Condition and Soil Moisture Analytics geospatial application provides access to high-resolution data from the SMAP mission and MODIS instrument in an easy-to-use format.

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Area, storage, and evaporation volume for Lake Mead, produced through a reservoir surface area algorithm, based on image classifications of Near-Infrared reflectance from NASA's Terra MODIS instrument. Credit: NASA

LP DAAC released new monthly Terra and Aqua MODIS Water Reservoir data products (MOD28/MYD28). These datasets provide a monthly time series of reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume for 151 man-made reservoirs and 13 regulated natural lakes across the globe.

Water budgets for individual watersheds can be estimated using remote sensing data for precipitation, evapotranspiration, and runoff. All of the data can be obtained from the GLDAS at the same temporal and spatial resolution through Giovanni. A few things to consider: note the units—calculations may have to be done in a GIS system to change to the units needed. For example, precipitation and ET are in kg m2/s; for annual data, you would need to multiply the data by 3600 s/hr, by 24 hr/day, and then by 365 days/year. Runoff data are in the same units above but are collected at 3-hour intervals and so need to be multiplied by 8 (3 hr/day) and then by 365 days/year. Once the data are in the appropriate units, you can use the raster calculation tool to subtract ET and runoff from precipitation to get an estimated water budget. Numerous statistical analyses available within a GIS program can provide additional information on trends.

LakePy is a pythonic user-centered front-end to the Global Lake Level Database, which delivers lake water levels for some 2000+ lakes scattered across the globe. Data comes from three sources: USGS National Water Information System, USDA Foreign Agricultural Service's Global Reservoirs and Lakes Monitor Database, and Theia's Hydroweb Database. The site contains a walk-through Jupyter Notebook as well.

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U.S. Fish and Wildlife Service's (FWS) National Wetlands Inventory web mapping application. Credit: FWS

The National Wetlands Inventory, established by the US Fish and Wildlife Service, provides a nationwide inventory of U.S. wetlands to provide biologists and others with information on the distribution and type of wetlands to aid in conservation efforts.

 

Last Updated
Feb 11, 2021