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  4. Agricultural and Water Resources Data Pathfinder: Vegetation Data

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 remote sensing data, view the Agricultural and Water Resources Data Pathfinder page.

Vegetation Greenness

Vegetation Greenness

NDVI time series of nearly four years of HLS data for three Indigo farms from the Midwest US. The different colored points each represent a different crop type, including corn, soy, wheat, and cover crops. The red shaded background represents a range of NDVI from time series for nearby fields that do not use Indigo seeds.

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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Land Cover and Crop Extent

Land Cover and Crop Extent

​MODIS Land Cover Type​ as seen in the visualization tool Worldview.

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.

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.

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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Evapotranspiration

Evapotranspiration

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.

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:

ECOSTRESS evapotranspiration daily data over Wisconsin, United States, acquired May 29, 2021.

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

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.

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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Evaporative Stress Index Data

Evaporative Stress Index Data

​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 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. See the Tools for Data Access and Visualization section for additional information on this tool.

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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

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, for some datasets, 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, 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 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 remote sensing 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 an Earthdata Login 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

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.

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Published November 19, 2019

Page Last Updated: Aug 24, 2021 at 1:38 PM EDT