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Agricultural and Water Resources Data Pathfinder: Vegetation Data

A field near Moscow, Idaho

A field near Moscow, Idaho. Image Credit: USDA

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.

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

Vegetation Greenness

Vegetation Greenness

Screenshot of Normalized Difference Vegatation Index of King Fire area of burn.

False-color image of Normalized Difference Vegetation Index (NDVI) data of King Fire area, September 2013 (left) and Nov 2014. (ORNL DAAC)

Vegetation indices have been developed to measure the amount of green vegetation over a given area and can be used to assess vegetation health. One commonly used vegetation index is the Normalized Difference Vegetation Index (NDVI), which 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. Increasing NDVI values indicate greener vegetation, including things like forests, croplands, and wetlands. The enhanced vegetation index (EVI) minimizes canopy-soil variations and improves sensitivity over dense vegetation conditions. Vegetation products from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (on the Aqua and Terra satellites) and the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite can be accessed in various ways.

Science-quality surface reflectance data products can be accessed directly via Earthdata Search; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

The Land Processes Distributed Active Archive Center’s (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS) offers the ability to extract subsets, transform, and visualize MODIS and VIIRS vegetation-related data products. The Oak Ridge National Laboratory DAAC (ORNL DAAC) subsetting tools provide a means to simply and efficiently access MODIS and VIIRS vegetation-related data products as well.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) 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 type of plot, 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.

NRT data can be accessed via 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 within Worldview.
  • MODIS EVI in Worldview
    This dataset is monthly at 1 km spatial resolution. Rolling 8-day and 16-day data are also available within Worldview.

Land Cover Type

Land Cover Type

Terra and Aqua MODIS Land Cover Type data product provides global land cover types at yearly intervals. The product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. This layer defines land cover type based on the International Geosphere-Biosphere Programme (IGBP) classification scheme.

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

  • Global Food Security-support Analysis Data 30 meter (GFSAD30) Cropland Extent data product; and
  • Global Hyperspectral Imaging Spectral-library of Agricultural crops for the Conterminous United States (GHISACONUS).

The GFSAD30 collection provides cropland extent data across the globe, divided and distributed into seven separate regional datasets, for the year 2015 (2010 for North America) at 30 meter resolution. These datasets are critical for policymaking and provide important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.

GHISACONUS provides dominant crop data (i.e., rice, corn, soybeans, cotton, and winter wheat) based on Earth Observing-1 (EO-1) Hyperion hyperspectral data. These data were acquired from 2008 to 2015 for the seven agroecological zones in the United States. Crop growth states (emergence/very early vegetative, early and mid-vegetative, late vegetative, critical, maturing/senescence, and harvest) for the major agricultural crops are also included in the spectral library.

The U.S. Department of Agriculture’s interactive tool 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 validation from ground-based measurements.

The U.S. Department of Agriculture also has a global crop explorer, which provides information by region or by crop commodity.

Evapotranspiration

Evapotranspiration

The combination of a plant's evaporation and transpiration is evapotranspiration, abbreviated ET. This parameter approximates the consumptive use of a landscape’s plants.

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 Credit: U.S. Geological Survey

Measurements of evapotranspiration (ET), the sum of evaporation from land surface and transpiration in vegetation, is extremely useful in monitoring and assessing water availability, drought conditions, and crop production. One of the issues in acquiring ET data is that it can’t be measured directly with satellite instruments as it is dependent on many other variables, such as land surface temperature, air temperature, and solar radiation. However, there are Level 4 data products (see data processing levels for more information) that incorporate daily meteorological reanalysis data with remotely-sensed data to arrive at estimations of ET. MODIS has such a product.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) aboard the International Space Station (ISS) measures the temperature of plants to better understand how they respond to the stress of insufficient water availability. ECOSTRESS was launched in July 2018 and uses a multispectral thermal infrared radiometer to measure radiance, which is converted to show surface temperature and emissivity of the land’s surface. ECOSTRESS has Level 3 ET data products.

The Land Data Assimilation System (LDAS) provides model-based ET data of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index, and then uses those inputs to model output estimates of soil moisture and evapotranspiration. When calculating ET, there are biases around seasonality or local-specific effects but developers try to account for those and calibrate accordingly; estimates 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.

Science quality ET data products can be accessed directly via Earthdata Search or the LP DAAC Data Pool; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

AρρEEARS offers the ability to extract subsets, transform, and visualize MODIS and ECOSTRESS ET data products.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map 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 access the desired dataset. 3) 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 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.

Evaporative Stress Index

Evaporative Stress Index

Level 4 Evaporative Stress Index PT-JPL, average from August 5, 2018 captured over California's Central Valley. High ESI is in shades of green and low ESI in shades of red.

Level 4 Evaporative Stress Index PT-JPL, average from August 5, 2018, captured over California's Central Valley. High ESI is in shades of green and low ESI in shades of red. Image Credit: LP DAAC

ECOSTRESS also has Level 4 evaporative stress index (ESI) and water use efficiency (WUE) products. 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 granule is acquired. Level 2 land surface temperature and emissivity products are also available.

AρρEEARS offers the ability to extract subsets, transform, and visualize ECOSTRESS L2-L4 data products.

Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AρρEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite

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 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.

Earthdata Search customization tools diagram.

Panoply

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.

HEG

The National Snow and Ice Data Center DAAC (NSIDC DAAC) has an HDF to GeoTIFF conversion tool (HEG), which allows you to geolocate, subset, stitch, and re-grid certain HDF-EOS datasets.

Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are several 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 the NASA GES DISC 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 Earth Observing System Data and Information System (EOSDIS) Worldview mapping 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 data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. The post-hurricane image on the left shows widespread outages around San Juan, including key hospital and transportation infrastructure.

AρρEEARS

AρρEEARS, from LP DAAC, offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AρρEEARS 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). The .shp, .shx, .dbf, or .prj files 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, WELDUSMO), 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 AρρEEARS.

Extracting an area in AppEEARS

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF-4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF-4 is selected, outputs will be grouped into .nc files 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 AρρEEARS 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 GeoTIFF or netcdf-4 files.

Soil Moisture Visualizer

The 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 (figure legends, top left and bottom right) at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data from NASA’s Soil Moisture Active Passive (SMAP) Observatory 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. Daily precipitation values for the site (purple spikes) are also provided for reference.

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 an Earthdata account to request data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 2000+ 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.
  • 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

Last Updated: Dec 6, 2019 at 2:25 PM EST