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Agricultural and Water Resources Data Pathfinder

Data pathfinders are pathways to the most commonly-used datasets within NASA’s Earth science collections. Data pathfinders have been developed to aid new data users in discovering data or visualizations of the data. While there are numerous datasets for any agricultural- or water-related measurement, these pathfinders provide direct links to data that have been used previously to aid in decision making.

Drought ranks in the top for weather-related disasters; the economic impacts associated with compromised water availability and food production are devastating for countries. As such, its critical for water resource managers and agricultural decision makers to monitor drought conditions. A combination of ground- and satellite-based data provides a unique view of the globe to better understand the impacts of climate change events. These measurements help scientists, researchers, and decision makers in forecasting events and assessing conditions in near real-time 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. All of these 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.

Applications of Data

Scientists, researchers, land managers, decision makers, etc. use remotely sensed data in numerous ways (to see some of the data in action, check out our Data User Profiles or our Freshwater Feature Articles). Satellite imagery coupled with ground-based data aids in water allocation, agricultural and 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.

When forecasting future events or responding to current events, there are three primary areas of focus: the land, the water, and the vegetation. With land surface, we can assess reflectance, temperature, and possible runoff. With water, we can look at precipitation, snow water equivalent, groundwater, and soil moisture, whether from a water availability standpoint or an assessment of irrigation strategies. With vegetation, we can assess vegetation health and phenology through Normalized Difference Vegetation Index (NDVI) and evapotranspiration via modeling processes.

When available, NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public within 3 hours of satellite overpass, which allows for almost near-real time (NRT) monitoring and decision making.

Applications for the use of NASA Earth science data in agricultural monitoring

Choose the application area of interest below.


Benefits and Limitations of Data

Benefits and Limitations of Data

In determining whether or not to use remotely sensed data, it’s important to understand both the benefits but also the limitations of the data. The United States is fortunate to have numerous ground-based measurements for assessing water storage, precipitation, atmospheric particulate matter, and more. However, this is not the case in other countries and even in the more rural areas of the United States. Satellite data provides more regional to global spatial coverage; some of the information is available in near real-time allowing for more efficient response. With satellite data, assessments can be made regarding the land surface, runoff, irrigation needs, and crop health. In addition, incorporating satellite data with in-situ data into modeling programs makes for a more robust and integrated forecasting system.

There are limitations to using satellite data in water availability and agricultural assessments, however. While the data provides a more global view, for field level events the spatial resolution is too coarse, introducing a higher level of error. In addition, many of the satellites only pass over the same spot every 1-2 days or sometimes as seldom as every 16+ days. In addition, the passive satellites (those that use energy being emitted from Earth for measurements) are not able to penetrate cloud or vegetation cover. This challenge can lead to data loss or errors in data quality. Finding the right instrument or understanding the modeling processes for your area of interest is key.

Land

LandLand
Land surface reflectance is useful in determining phenological transition dates including start of season, peak period, and end of season. Two instruments are primarily used for this measurement: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). MODIS has a spatial resolution of 1km, whereas VIIRS has an improved spatial resolution of 375m. MODIS data are acquired daily. Landsat 8 also has an instrument that provides surface reflectance; it is available at 30m spatial resolution, acquired every 16 day. Land Surface Temperature is useful for monitoring changes in weather and climate patterns and used in agriculture and water resource management to allow farmers and decision makers to evaluate water requirements.
  • Science quality, or higher-level “standard” data products can be accessed via the Earthdata Search client; MODIS and VIIRS data sets are available as HDF files and can be opened using Panoply, but are also customizable to GeoTIFF:
Runoff is very important when thinking about water resources and agricultural management, especially after storm events and wildfires. Runoff can impact water quality with chemicals (from fertilizers and stormwater runoff), debris, and waste products entering water bodies. NASA cannot measure runoff directly; however, information that can be used to assess predicted runoff can be measured using remote sensing. These data are then input, along with ground-based data, into atmosphere-land models to estimate runoff, the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a US national collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, leaf area index, etc. and then uses those inputs to model output estimates of runoff, evapotranspiration, etc.
  • NLDAS (US) Runoff Data in Giovanni
    Select a map plot (you can generate a time-averaged map, an animation, or seasonal maps), date range and region, and determine 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, and determine your variable and then plot the data (data are in multiple temporal resolutions, so be sure to note the starting and end date to ensure you access the desired dataset). Data can be downloaded as GeoTIFF.

Water

WaterWater
Precipitation NASA’s Precipitation Measurement Missions provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM). With the follow-on GPM mission, measurements increased in accuracy, improved detection of light rain and snow, and extended the spatial coverage. These products are available individually or have been integrated, TRMM into the TRMM Multi-satellite Precipitation Algorithm (TMPA) and GPM into the Integrated Multi-satellitE Retrievals for GPM (IMERG), with a global constellation of satellites to yield improved spatial/temporal precipitation estimates with a temporal resolution of 30 minutes. IMERG’s multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours for flash flood events, Late = 12 hours for crop forecasting, and Final = 3 months for research with the incorporation of rain gauge data). In addition, for NRT data, among IMERG and LDAS, the Atmospheric Infrared Sounder (AIRS) instrument provides an estimate of daily precipitation measured in millimeters (mm) using cloud-related parameters of cloud-top pressure, fractional cloud cover, and cloud-layer relative humidity.
  • Science quality, or higher-level “standard” data products can be accessed via the Earthdata Search client:
    • TMPA at Earthdata
      Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at 3 hours and 1 day. Data are in HDF format and can be opened using panoply. Data are available from 1997.
    • IMERG at Earthdata
      Early, Late and Final precipitation data on the half hour or 1 day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2014.
  • Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, and time series. through an online interactive tool, Giovanni. For more information on choosing a type of plot, see Giovanni’s user manual.
    • TMPA in Giovanni
      Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF.
    • IMERG in Giovanni elect a map plot (you can a time-averaged map, an animation, or seasonal maps), date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF. Data are only available from 2014-2018.
  • NRT Data (unsure about which of the NRT to choose, see diagram at right):
Snow Cover/Snow Water Equivalent
Snow cover is the presence of snow over land and water bodies and measurements are acquired during day time and under cloud clear conditions. Snow Water Equivalent indicates the amount of water that is contained in snowpack in the Northern and Southern Hemispheres measured in millimeters (mm). Another way to think of it is if all the snow were melted instantaneously, it would be the depth of that water. These measurements are both useful for assessing surface runoff when the snow melts and to assess water availability for regions in lower elevations. The Terra MODIS instrument measures snow cover and the Advanced Microwave Scanning Radiometer measures snow water equivalent. Groundwater

Changes in groundwater storage can be measured from space using the Gravity Recovery And ClimateTGraph showing an overall decline in groundwater storage over timeExperiment (GRACE) data. Data are available from 2002 to the present and are anomalies, changes from the mean and so not representative of total water storage. Note that there are several limitations with the GRACE data. Limitations: the scale of the data is 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 the satellite only measures in 1 dimension; and GRACE does not provide information on whether the aquifer is confined or unconfined. The value comes in doing more regional studies to determine general trends in groundwater storage.

  • Science quality, or higher-level “standard” data products can be accessed via the Earthdata Search client; data sets are available as NetCDF files which can be opened using Panoply or imported into some GIS programs.
    • Groundwater Storage Percentile at Earthdata
      This serves as a drought indicator and the data are based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.
  • Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, and time series through an online interactive tool, Giovanni. For more information on choosing a type of plot, see Giovanni’s user manual.
  • GRACE observations are acquired through two formation-flying satellites. In processing the data, scientists must run a complex inversion algorithm in combination with precise GPS information and acceleration corrections. Many parameter choices and solution strategies are possible. As such, three different teams have taken on this task—GeoforschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas, Austin (CSR), and the Jet Propulsion Laboratory (JPL)—all producing slightly different results. Recent peer-reviewed papers found that the simple arithmetic mean of JPL, CSR, GFZ was most effective. For more information on the three different solutions, see the JPL GRACE website on Choosing a Solution. Data are represented as Water Equivalent Thickness (WET), which is a way of representing changes in the gravity field in hydrological units.
  • GRACE Groundwater WET
    Monthly data from 2002-2017 from each solution in ASCII, or GeoTIFF. The NetCDF data are averaged 2002-2017 data. The mean of the three can be calculated in most GIS programs (see figure).

Grace data from the three different algorithms and an averaged version

Soil Moisture
There are numerous means of accessing information on soil moisture. As mentioned in the benefits and challenges, current ground

Soil moisture as compared to crop yield. Graph indicates that the two variables have a positive correlation

measurements of soil moisture are sparse and have limited coverage, and so satellite data can help fill in those blanks. Specifically related to agriculture, variation in soil moisture, regardless of the data resolution, correlates very well with variation in crop yield (see diagram to right). However, satellites are not able to get at the detail, or resolution, needed. The preferred measurement should be chosen based upon your needs. NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top 5cm of the soil globally every 3 days, at a resolution of 10-40km. NASA, in collaboration with other agencies, has also developed models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a US national 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 soil moisture and evapotranspiration.
  • Science quality, or higher-level “standard” data products can be accessed via the Earthdata Search client; data sets are available as HDF5 files which can be opened using Panoply.
  • Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, and time series through an online interactive tool, Giovanni. For more information on choosing a type of plot, see Giovanni’s user manual.
    • NLDAS (US) Data in Giovanni
      Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF.
    • GLDAS (Global) Data from Giovanni — select a map plot, date range, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF.
  • NRT data can be accessed via Worldview or other NASA-related tools:

Vegetation

VegetationVegetation
Vegetation Density and Extent

The Normalized Difference Vegetation Index (NDVI) provides a means to assess the vegetation health in a given area. Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8). Aqua and Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data can be accessed via the following ways:

  • Science quality, or higher-level “standard” data products can be accessed via the Earthdata Search client; data sets are available as HDF files which can be opened using Panoply or customizable to GeoTIFF.
  • Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, and time series through an online interactive tool, Giovanni. For more information on choosing a type of plot, see Giovanni’s user manual.
    • MODIS NDVI in Giovanni
      Select a map plot, date range and region and then plot the data. Data can be downloaded as GeoTIFF.
  • NRT data can be accessed via Worldview:
    • MODIS NDVI
      This dataset has a spatial resolution of 250m and a temporal resolution of 8 days.
Crop Lands
The US Department of Agriculture’s has an interactive tool that provides crop-specific land cover data layers created annually for the continental United States using moderate resolution satellite imagery, specifically from Landsat, and extensive agricultural ground truth. Evapotranspiration (ET)

ET, the sum of evaporation from the land surface, but also through transpiration in vegetation, is extremely useful in monitoring and assessing water availability, drought conditions, and crop production. Unfortunately one of the issues in acquiring ET data is that it can’t be measured directly as it is dependent on so many other variables, like land and air temperature and solar radiation. It is easiest to use model data to get a sense of the ET in the area. These models are part of the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a US national collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, leaf area index, etc. and then uses those inputs to model output estimates of soil moisture and evapotranspiration. When calculating ET, there are obviously biases but developers try to account for those and calibrate accordingly; estimate of ET are provided every day and integrated to get monthly, seasonal or annual information within 2-12% error, which is adequate for most water management work.

  • Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, and time series through an online interactive tool, Giovanni. For more information on choosing a type of plot, see Giovanni’s user manual.
    • GLDAS ET in Giovanni
      Data are available with a temporal resolution of 3-hourly, daily and monthly. Select a map plot, date range and region and then plot the data. Data can be downloaded as GeoTIFF.
  • Google Earth Engine provides access to ET data products derived from Landsat data, through a portal called EEFlux. EEFlux processes individual Landsat scenes, at a 30m resolution, from any period from 1984 through present and for nearly every land area on the Globe. Note: Users should be wary of results for times or regions where reasonable endpoints for calibration are rare (e.g. winter in North America, the Amazon rainforest). For more information on calibration, see the FAQs at the site.
    • ET in EEFlux
      Higher ET areas somewhat correspond to lower surface temperatures and high NDVI
  • ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), a mission that launched July 2018 and has been acquiring data from the International Space Station, will have an ET product that will be released within the next few months.

Drought-Specific Resources

Drought- Specific ResourcesDrought-Specific Resources
There are several tools that consolidate a lot of this information at the US 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 US 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.
  • NASA USDA Global Soil Moisture Product provides soil moisture information across the globe at 0.25°x0.25° spatial resolution. These datasets include surface and subsurface soil moisture (mm), soil moisture profile (%), and surface and subsurface soil moisture anomalies.
  • 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) (North America, Europe and Asia) countries. 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.

Water Management Resources

Water Management Resources

Water Management Resources

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

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 - information such as population density, reservoirs, dams, agricultural lands, drought frequency and distribution, economic risk, and mortality risk, flood frequency and distribution, economic risk, and mortality risk, nitrogen and phosphorus fertilizer application, etc. These data sets are available as GeoTIFFs or ESRI Grid Files. Some of these are also available in Worldview.

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.


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, etc).


The Oak Ridge National Lab (ORNL) DAAC Soil Moisture Visualizer allows you to subset, view, create a time series, and download soil moisture data from across North America from SMAP, and a variety of other data sources. Numerous other datasets, such as DAYMET weather and climatological data and soil groups can be accessed through the ORNL Spatial Data Access Tool (SDAT).


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.

Tools for Data Access

Tools for Data Access

Earthdata Search provides a means of accessing all of NASA’s Earth science data across all distributed active archive centers. It provides the only means to access all data regardless of where the data are archived. Within Earthdata Search, you can subset using temporal and geographic constraints. Some data can be customized once the data of interest are selected; to do this, add the data of interest to your project and then click download all.

earthdata searchIn the project area, you can select to customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML or a GeoTIFF. You can also choose from a variety of projection options. Lastly you can subset the data, obtaining only the bands that are needed.customizable


subsetting

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.

The National Snow and Ice Data Center (NSIDC) has an HDF to GeoTIFF conversion tool, which allows you to geolocate, subset, stitch, and regrid certain HDF-EOS data sets.

Giovanni is an online (Web) environment for the display and analysis of geophysical parameters.


Page Last Updated: Jul 5, 2019 at 9:58 AM EDT