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Agricultural and Water Resources Data Pathfinder: Water Data

The Colorado River Basin lost nearly 53 million acre feet of freshwater over the past nine years, according to a new study based on data from NASA’s GRACE mission. This is almost double the volume of the nation's largest reservoir, Nevada's Lake Mead (pictured)

The nation's largest reservoir, Nevada's Lake Mead. Image credit: U.S. Bureau of Reclamation

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.

To read about the data or benefits and limitations of using remote sensing data, view the Agriculture and Water Resources Data Pathfinder page.

Precipitation

Precipitation

Map shows IMERG daily precipitation data in North America, from August 15 to August 30. Hurricanes Marco and Laura can be seen in the Gulf of Mexico starting August 21.

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.

Famine Early Warning System Network (FEWS NET)

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. See the Tools for Data Access and Visualization section for additional information on these tools.

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Snow Cover/Snow Water Equivalent

Snow Cover/Snow Water Equivalent

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.

Snow Water Equivalent over Tuolumne Basin June 4, 2017. Image credit: NASA Airborne Snow Observatory

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:

Map of Airborne Snow Observatory (ASO) lidar coverage of the Kings River basins during 17-21 April, 2019. Credit: NASA's Jet Propulsion Laboratory

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. See the Tools for Data Access and Visualization section for additional information on these tools.

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Groundwater

Groundwater

Screenshot showing map of world with oceans in a uniform blue and colors on continents and landmasses indicating changes in terrestrial water storage.

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.

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.

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

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.

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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Soil Moisture

Soil Moisture

Map, based on Soil Moisture Active Passive (SMAP) data, shows soil moisture anomalies, how much the moisture content was above or below the norm, in the United States in mid-May 2018. Credit: NASA Earth Observatory

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):

​Soil moisture as visualized with 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.​

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. See the Tools for Data Access and Visualization section for additional information on both of these tools.

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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Water Resources

Water Resources
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

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.

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

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

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.

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.

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Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite | Spatial Data Access Tool (SDAT)

Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the Earth science disciplines.

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

Screenshot of the Search Earthdata site.

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

Earthdata Search customization tools diagram.

Panoply

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

Giovanni

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

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

For more detailed tutorials:

  • Giovanni How-To's on GES DISC's YouTube channel.
  • Data recipe for downloading a Giovanni map, as NetCDF, and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text.

Worldview

NASA's EOSDIS Worldview visualization application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview now includes nine geostationary imagery layers from GOES-East, GOES-West and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the visualization of the differentiation between air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

Worldview Suomi NPP/VIIRS nighttime lights comparison image showing power outages caused by Hurricane Irma in September 2017. The right image (acquired 1 September 2017) shows the island before Hurricane Irma. The left image (acquired 9 September 2017) shows power outages across island after Hurricane Irma. Interactively explore this image in NASA Worldview. Image: NASA Worldview.

AppEEARS

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

Performing Area Extractions

After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you will specify a series of parameters that are used to extract data for your area(s) of interest.

Spatial Subsetting

You can define your region of interest in three ways:

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

Select the date range for your time period of interest.

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

Adding Data Layers

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

Extracting an area in AppEEARS

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF4

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

If GeoTIFF is selected, you must select a projection

Interacting with Results

Once your request is completed, from the Explore Requests page, click the View icon in order to view and interact with your results. This will take you to the View Area Sample page.

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

Interpreting Results in AppEEARS

Be sure to check out the AppEEARS documentation to learn more about downloading the output files in GeoTIFF or NetCDF4 format.

Soil Moisture Visualizer

ORNL DAAC has developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of different soil moisture datasets over North America. The visualization tool incorporates in-situ, airborne, and remotely-sensed data into one easy-to-use platform. This integration helps to validate and calibrate the data, and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide.

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

ORNL DAAC Soil Moisture Visualizer

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

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several MODIS and VIIRS Subset Tools for subsetting data.

  • With the Global Subset Tool, you can request a subset for any location on earth, provided as GeoTIFF and in text format, including interactive time-series plots and more. Users specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, you can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. You will need to register for an Earthdata Login to request data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 3000+ field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as National Ecological Observatory Network, Forest Global Earth Observatory network, Phenology Camera network, and Long Term Ecological Research network that are of relevance to the biodiversity community.
  • With the Web Service, you can retrieve subset data (in real-time) for any location(s), time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into users' workflow.

Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

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

Spatial Data Access Tool (SDAT)

ORNL DAAC's SDAT is an Open Geospatial Consortium standards-based web application to visualize and download spatial data in various user-selected spatial/temporal extents, file formats, and projections. Several data sets including land cover, biophysical properties, elevation, and selected ORNL DAAC archived data are available through SDAT. Files in KMZ format are also provided for data visualization in Google Earth.

Within SDAT, select a dataset of interest. Upon selection, the map service will open displaying the various measurements, with the associated granule, and a visualization of the selected granule.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

You can then select your spatial extent, projection, and output format for downloading.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

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Published November 19, 2019

Page Last Updated: Aug 24, 2021 at 1:39 PM EDT