SDG 2 Data Pathfinder

SDG 2: Zero Hunger Icon

Nearly one in 10 people globally were exposed to severe levels of food insecurity in 2019, according to the United Nations (UN). The vulnerabilities and inadequacies of global food systems are expected to further intensify over the coming years.

Through Sustainable Development Goal (SDG) 2, Zero Hunger, the UN proposes to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture by 2030. A critical aspect of this goal is monitoring food production and implementing agricultural practices that increase production while also maintaining ecosystems and strengthening the capacity for adaptation to climate change, extreme weather, drought, flooding, invasive species, and other disasters. NASA Earth observations are an integral component in providing data necessary to assess progress towards achieving these goals.

This false-color Landsat 8 Operational Land Imager (OLI) image acquired on December 26, 2018, highlights the patchwork of flooded rice fields along the Sacramento and Feather Rivers in California, USA. Inundated fields are shown in dark blue; vegetation is bright green. A series of raised levees form the grid pattern between the fields. This image was acquired using a combination of shortwave infrared, near infrared, and visible light (bands 6-5-4). Credit: NASA Earth Observatory.

This false-color Landsat 8 Operational Land Imager (OLI) image acquired on December 26, 2018, highlights the patchwork of flooded rice fields along the Sacramento and Feather Rivers in California, USA. Inundated fields are shown in dark blue; vegetation is bright green. A series of raised levees form the grid pattern between the fields. This image was acquired using a combination of shortwave infrared, near infrared, and visible light (bands 6-5-4). Image: NASA Earth Observatory.

SDG Goals are divided into broad Targets that are further divided into Indicators used to track progress toward accomplishing the Targets. NASA collects and analyzes data about our home planet applicable to agriculture and food production and makes these data fully and openly available to anyone. These data are helping us develop a better understanding of the connections between food production and land cover, soil moisture, evapotranspiration, the water cycle, temperature, and weather.

NASA helps develop tools to address food security and works with decision-makers and data users to tailor these tools to specific locations and user needs. These efforts help address issues like water management for irrigation, crop-type identification and land use, coastal and lake water quality monitoring, drought preparedness, and famine early warnings. Much of this work is carried out and supported fully or in part by the agency's Applied Sciences Program, which works with individuals and institutions worldwide to inform decision-making, enhance quality of life, and strengthen our economy. The Applied Sciences Program co-leads the international Earth Observations for Sustainable Development Goals initiative launched by the Group on Earth Observations. The initiative advances global knowledge about effective ways that Earth observations and geospatial information can support the SDGs.

The data and resources in this Pathfinder are specifically related to SDG 2 Targets 2.1, 2.3, and 2.4 (described below). Additional information about NASA data and products related to agriculture, water resources, and similar topics is available in the Agriculture and Water Resources Data Pathfinder.

SDG Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture

Target 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious, and sufficient food all year round.

Nearly 85% of Mexico is facing drought conditions as of April 15, 2021. This image shows Evaporative Stress Index (ESI) data for the country, with brown colors indicating drier conditions. ESI incorporates leaf area index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Aqua and Terra satellites with observations of land surface temperatures from NOAA satellites and observations. The observations are used to estimate the amount of water evaporating from the land surface and from the leaves of plants. Image: NASA Earth Observatory; Landsat Image Gallery.

Nearly 85% of Mexico is facing drought conditions as of April 15, 2021. This image shows Evaporative Stress Index (ESI) data for the country, with brown colors indicating drier conditions. ESI incorporates leaf area index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Aqua and Terra satellites with observations of land surface temperatures from NOAA satellites and observations. The observations are used to estimate the amount of water evaporating from the land surface and from the leaves of plants. Image: NASA Earth Observatory; Landsat Image Gallery.

Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists, and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment.
Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters and that progressively improve land and soil quality.

The NASA datasets listed in the following sections help measure progress toward meeting the above SDG 2 Targets by providing Earth observations that aid in monitoring the food insecurity of vulnerable populations, tracking agricultural production related to incomes of small-scale food producers, and monitoring environmental impacts to soil, water, fertilizer and pesticide pollution, and biodiversity. While not designed to be a complete list of all salient resources available through NASA's Earth science collection, the following information about NASA data, products, and services will help you chart a path to finding the information you need to help address SDG 2 Targets.

Land Surface Reflectance | Land Cover | Vegetation Greenness | Gross Primary Productivity | Evapotranspiration | Evaporative Stress Index | Precipitation | Groundwater | Snow Water Equivalent | Soil Moisture | Land Surface Temperature | Socioeconomic | Water Quality

Land Surface Reflectance Data

Land Surface Reflectance Data
September 10, 2009, Landsat image of farmland across northwest Minnesota

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

Land surface reflectance is a measure of the fraction of incoming solar radiation reflected from Earth's surface to a satellite-borne or aircraft-borne sensor. It is useful for measuring the greenness of vegetation, which can then be used to determine phenological transition dates including the start of the growing season, the period of peak growth, and the end of the growing season. Agricultural production estimates must be restricted to crop-specific areas to avoid confusion with other crops, natural vegetation, and areas of no vegetation. This allows specific crops to be followed through time with continued observations using sustained land imaging and multi-spectral high-resolution imagery.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument aboard NASA's Terra satellite is a high-resolution instrument that acquires visible and near-infrared (VNIR) reflectance data at 15 m spatial resolution and short wave infrared (SWIR) reflectance data at 30 m spatial resolution. A cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry, ASTER data are distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC). As a tasked sensor, ASTER acquires data when it is directed to do so over specific targets. This makes its temporal resolution variable depending on the requested target region of interest. ASTER surface reflectance products are processed on-demand and can be requested through Earthdata Search (NOTE: there is a limit of 2,000 granules per order):

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

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

Multi-temporal Enhanced Vegetation Index-2 information from HLS for an area of irrigated cropland of Central California (near Los Banos). The colors represent mean EVI2 for three periods of the 2018 growing season: Red areas peak early in the season, Green areas peak in the middle, and Blue areas peak late.

Multi-temporal Enhanced Vegetation Index-2 (EVI2) information from HLS for an area of irrigated cropland of Central California (near Los Banos), USA. The colors represent mean EVI2 for three periods of the 2018 growing season: Red areas peak early in the season, Green areas peak in the middle, and Blue areas peak late. 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: Landsat/Sulla-Menashe, et al.

Another high-resolution land surface reflectance imagery option is Harmonized Landsat Sentinel (HLS)-2 imagery. HLS imagery provides consistent surface reflectance and top of atmosphere brightness data from the OLI aboard Landsat 8 and the Multi-Spectral Instrument (MSI) aboard the European Space Agency Sentinel-2A and -2B satellites. The harmonized measurement enables global land observations every 2-3 days at 30 m spatial resolution. HLS data are currently provisional and not considered standard data products. A science quality HLS dataset is expected to be released in fall 2021.

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

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Land Cover and Crop Extent Data

Land Cover and Crop Extent Data

​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 UN 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 (GHISA) 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 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 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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Vegetation Greenness Data

Vegetation Greenness Data

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 image of Australia acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA’s Terra satellite on August 18, 2021. Darker green colors indicate greener, healthier vegetation. Note how the desert in the middle of Australia shows little to no vegetation. Interactively explore this image using NASA Worldview. Image: NASA Worldview.

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 the 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 HDF files 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 vegetation-related data products produced from data acquired by the MODIS and VIIRS instruments. AppEEARS allows users to subset data by defining specific points or areas 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 about 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
    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
    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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Gross Primary Productivity Data

Gross Primary Productivity Data

Terra MODIS gross primary productivity (GPP) data over central South America on August 29 - September 5, 2018.

Terra MODIS gross primary productivity (GPP) data over central South America on August 29 - September 5, 2018. Image: NASA Land Processes DAAC.

Gross primary productivity (GPP) is the total energy captured by vegetation. Net primary productivity (NPP) is the amount of carbon dioxide (CO2) vegetation takes in during photosynthesis minus how much CO2 is released during plant respiration. Values typically range from 0 to 6.5 grams per square meter per day. A negative value indicates decomposition or that respiration overpowered carbon absorption, that is, more carbon was released to the atmosphere than was taken in. Estimates of GPP provide valuable information about the spatial distribution and temporal variability of primary production, which in an agricultural setting determines crop yields and fodder production for animals.

Level 4 GPP and NPP products produced from data acquired by the MODIS instrument aboard the Terra and Aqua satellites are available in yearly and 8-day temporal resolutions with 1 km or 500 m pixel size using Earthdata Search:

AppEEARS, available through 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 points or areas of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF-4 (area) formats.

In addition, MODIS and VIIRS subsetting tools available through ORNL DAAC, provide a means to access and visualize MODIS and VIIRS GPP and NPP data products simply and efficiently. See the Tools for Data Access and Visualization section for additional information about these tools.

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Evapotranspiration Data

Evapotranspiration Data

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 Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 product.

Research quality MODIS Level 4 ET products are available in yearly and 8-day temporal resolutions with 500 m pixel size and can be accessed 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 as HDF files 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 points or areas of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF-4 (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 about 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 evapotranspiration (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. ESI data can be used to assess agricultural drought and observe vegetation stress.

Level 4 ESI and water use efficiency (WUE) products created from data, collected by ECOSTRESS, 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. 

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 points or areas of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF-4 (area) formats. See the Tools for Data Access and Visualization section for additional information about this tool.

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Precipitation Data

Precipitation Data

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 the GPM website.

In addition to the precipitation products developed by NASA's 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 time step 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 the NASA 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 the FLDAS project 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 an available data record 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) Check the box of the variable in the left column you want to include. 2) Select a plot type. 3) 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). 4) Select a region of interest using a bounding box, shapefile, or geographic coordinates. 5) 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 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 AppEEARS 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 about these tools.

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Groundwater Data

Groundwater Data

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 UN, water use has been growing globally at twice the rate at which 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 (DLR) 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).

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 NASA's Jet Propulsion Laboratory (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 as NetCDF files that can be opened using NASA's Panoply data viewer 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 netCDF file into single GeoTIFF files for each month.

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.

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 (CRI) filter that reduces leakage errors across coastlines.

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

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Snow Water Equivalent Data

Snow Water Equivalent Data

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. Snow cover is one measurement acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra satellite. 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 as HDF5 files that can be opened using NASA's Panoply data viewer):

  • MODIS Snow Cover
    Data are available from 2000 to present in daily, 8-day, or monthly at various resolutions. Datasets are customizable to GeoTIFF.
  • AMSR-E SWE
    Data are available from 2002 to 2011 in daily, 5-day, and monthly timeframes. Datasets are customizable to GeoTIFF.
  • AMSR2 SWE
    Data are available from February 2018.

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.

The Airborne Snow Observatory (ASO) mission collects data on snow melt flowing out of major water basins in the Western U.S. The mission began in April 2013 as a collaboration between 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. The 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 AppEEARS 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 about these tools.

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Soil Moisture Data

Soil Moisture Data

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.

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 NASA's Land Data Assimilation System (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 SMAP data products can be accessed using Earthdata Search (datasets are available as HDF5 files 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 points or areas 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 about 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 U.S. Department of Agriculture's National Agricultural Statistics Service, NASA, and George Mason University. The Crop Condition and Soil Moisture Analytics (Crop-CASMA) geospatial application provides access to high-resolution data from the SMAP mission and from the MODIS instrument in an easy-to-use format.

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Land Surface Temperature Data

Land Surface Temperature Data

Satellite images show the relationship between the characteristics of a landscape, and day and night surface skin temperature. Heavily forested areas remain relatively cool throughout the day, while barren and arid areas can be tens of degrees warmer. These images were acquired in the early morning and afternoon of July 6, 2011.

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

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

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

To quickly extract a subset of ECOSTRESS, MODIS, or VIIRS data for a region of interest, use AppEEARS available through LP DAAC, or the subsetting tools available through ORNL DAAC. See the Tools for Data Access and Visualization section for additional information on both of these tools.

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

LST data also are produced as part of the joint NASA/USGS Landsat series of Earth observing missions (note: you will need a USGS Earth Explorer login—which is separate from NASA's Earthdata Login—to download Landsat data):

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Socioeconomic Data

Socioeconomic Data

Global croplands map the of each 10 km grid cell land area that is under cropland. The dataset was generated with the MODIS land cover product, the Satellite Pour l'Observation de la Terre (SPOT) VEGETATION's global land cover 2000 product, combined with the U.N. Food and Agriculture (FAO) agricultural statistics. Credit: CIESIN SEDAC

This map from the SEDAC Croplands, v1 (2000) collection shows the land area dedicated to cropland in Asia. Color shading indicates the percentage of the grid cells under cropland, with darker colors indicating a higher percentage under cropland. Image: NASA SEDAC.

NASA's Socioeconomic Data and Applications Center (SEDAC) is NASA's Earth Observing System Data and Information System (EOSDIS) DAAC responsible for archiving and distributing socioeconomic data in the EOSDIS collection. SEDAC is hosted at Columbia University's Center for International Earth Science Information Network and serves as an "Information Gateway" between the socioeconomic and Earth science data and information domains.

SEDAC includes a number of data collections relevant to food security and agriculture, including:

  • Past, present, and future population distribution
  • Reservoirs and dams
  • Agricultural land coverage/acreage/area
  • Drought frequency and distribution
  • Economic and mortality risks from natural disasters
  • Flood frequency and distribution
  • Food insecurity hotspots
  • Agricultural pesticide use
  • Nitrogen and phosphorus fertilizer application
  • Global roads
  • Environmental performance indicators

Agriculture and Food Security Data in Earthdata Search

Agriculture and Food Security Data at SEDAC

These datasets are available as GeoTIFFs or Esri Grid Files. Many of these datasets can be visualized using the SEDAC Map Viewer.

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Water Quality Data

Water Quality Data

Water quality is as important to food production as water quantity but is harder to measure from space because many of its characteristics are invisible. Fresh, clean water is needed for agricultural production while fresh or salt water within a balanced, healthy ecosystem is critical for aquaculture as a sustainable food source. Agricultural practices that have increased use of fertilizers have led to more nitrogen and phosphorous runoff, which can lead to increased algal and plant growth that robs these aquatic ecosystems of oxygen, a condition called eutrophication. The Earthdata Water Quality Data Pathfinder provides more detailed information on the use of NASA data for assessing water quality in large water bodies along with links to these data.

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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 NASA's EOSDIS tool for searching and discovering data in the EOSDIS collection 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, users can customize granules. Users can reformat the data and output as HDF, NetCDF, ASCII, KML, or a GeoTIFF and can choose from a variety of projection options. Data can be subset to obtain 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.

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:

Worldview

NASA's Worldview visualization application visualization application provides the capability to interactively browse over 950 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 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 also 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 visualization of specific 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 at 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 requesting an area extraction, users are taken to the Extract Area Sample page where they specify a series of parameters that are used to extract data for the areas of interest.

Spatial Subsetting

Define the region of interest in one of three ways:

  • Upload a vector polygon file in shapefile format (a single file with multiple features or multipart single features can be uploaded). The .shp, .shx, .dbf, or .prj files must be zipped into a file folder to upload.
  • Upload a vector polygon file in GeoJSON format (users 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 the time period of interest.

Specify the range of dates for which data are desired for extraction 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 the 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 .nc files by product and by feature.

If GeoTIFF is selected, you must select a projection

Interacting with Results

From the Explore Requests page, click the View icon to view and interact with results. This will take users 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 the feature contains attribute table information, users 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

Please see the AppEEARS documentation to learn more about downloading the output as GeoTIFF or NetCDF4 files.

Soil Moisture Visualizer

ORNL DAAC 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 selecting a 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. The visualizer also 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 tools for subsetting data from the MODIS and VIIRS instruments:

  • With the Global Subset Tool, users can request a subset for any location on Earth that are 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, users 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 Sites Subsets Tool, users can download pre-processed subsets for more than 3,000 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.
  • With the Web Service, users can retrieve subset data (in real-time) for any location, 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 a 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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Other Resources

Other Resources

NASA Applied Sciences Resources

Image shows individual measuring wheat in a field in Punjab, Pakistan.

Professor Matt Hansen is using commercial small satellite imagery to map winter wheat extent in Punjab, Pakistan. The project is incorporating field measurements to validate crop maps. Image: NASA Harvest.

NASA's Applied Sciences Agriculture Program Area promotes the use of Earth observations to strengthen food security, support market stability, and protect human livelihoods. NASA Harvest, which is managed under the Agriculture Program Area, 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 both domestically and globally. NASA Harvest has prioritized three impact areas: agricultural land use, agricultural sustainability, and agricultural productivity, and aims to improve each of these areas as well as the methods and products that provide actionable information and insight about them from farm to global scales. Together with partners in the U.S. and around the world, they help bolster food security, improve agricultural resilience, and reduce price volatility for vulnerable communities.

NASA's Applied Sciences Water Resources Application Area supports partnerships and applied research to discover, develop, and demonstrate new practical uses for NASA's Earth observations by the water resources management community. They work with a wide range of partners in the U.S. and around the world to find innovative solutions as shifts in land use, changing climates, and growing populations stress water supplies. The Water Resources Applications Area also supports the NASA Western Water Applications Office (WWAO), which works closely with water resource management partners in the Western U.S., and NASA Harvest, which engages with both domestic and international partners in the agricultural sector.

NASA's Applied Sciences Program supports two additional food security-related initiatives:

  • The Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Crop Monitor For Early Warning provides monthly transparent, multi-source, consensus assessments of crop growing conditions, status, and agro-climatic conditions that are likely to impact production in countries vulnerable to food insecurity. These assessments help strengthen agricultural, humanitarian intervention, and food security decision making and policy implementations.
  • The Drought Severity Evaluation Tool (DSET) was developed as part of NASA's Navajo Nation Drought Project. A collaborative effort of WWAO, the Navajo Nation Department of Water Resources, and the Desert Research Institute, DSET improves drought reporting and monitoring in the Navajo Nation, which covers an area in northern Arizona, southern Utah, and northern New Mexico roughly the size of the U.S. state of West Virginia.

NASA's Applied Remote Sensing Training (ARSET) program, which is part of the Applied Sciences Capacity Building Program Area, trains people to use Earth-observing data for environmental management and decision-making. ARSET training programs relevant to this SDG are:

Other NASA Assets

Annual total harvested crop area from the Carbon Fluxes from Global Agricultural Production and Consumption, 2005-2011 dataset, visualized in Panoply.

Annual total harvested crop area from the Carbon Fluxes from Global Agricultural Production and Consumption, 2005-2011 dataset. Darker green indicates a larger total annual harvested crop area in km2/year. Credit: NASA ORNL DAAC.

Data collections and resources relevant to this SDG are available through ORNL DAAC:

  • The Global Database of Soil Respiration Data provides soil respiration measurements that encompass the flux of autotrophically- and heterotrophically-generated CO2 from the soil to the atmosphere.
  • The Carbon Fluxes from Global Agricultural Production and Consumption dataset, which is part of NASA's Carbon Monitoring System (CMS), provides global estimates of carbon fluxes associated with annual crop NPP and harvested biomass, annual uptake and release by humans and livestock, and the total annual estimate of net carbon exchange derived from these carbon fluxes.
  • The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) campaign is focused on observations of root zone soil moisture and net ecosystem exchange of CO2 over a variety of North American biomes across several seasons.
  • The Soil Moisture Visualizer harmonizes surface and root zone soil moisture datasets that are in diverse native data formats and encompass a range of spatial footprints, soil depths, and measurement frequencies.

Monitoring Drought using NASA Earth Observation Data is a story map from NASA’s ArcGIS DAAC Collaboration. The story map incorporates NASA Earth observations from multiple NASA programs into one thematic GIS web map. Analysis of these datasets provides a means to monitor in near real-time conditions leading to and resulting from drought as well as how humans may be affected. Datasets in the story map include precipitation, soil moisture, vegetation surface reflectance, evaporative stress, normalized difference vegetation index (NDVI), and population density.

The NASA-funded Methane Source Finder project mapped potential sources of methane in the state of California and developed new technologies to make remote sensing data of methane emissions readily available. Additional funding for this effort was provided by the California Air Resources Board, the California Energy Commission, the National Institute of Standards and Technology, Rocky Mountain Institute, and the University of Arizona. Read more about this effort at From Cow Manure to Landfills: Mapping Methane in California.

External Resources

Several external tools consolidate food security and agriculture information at the U.S. national level and at the global level:

    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 System Network, which was created in 1985 by USAID, provides early warning and analysis on acute food insecurity. Analysts and specialists in 22 field offices work with U.S. government science agencies, national government ministries, international agencies, and non-governmental organizations to produce forward-looking reports on more than 36 of the world’s most food-insecure countries.
  • NOAA's National Integrated Drought Integration System provides drought-related information and resources and also has a suite of data, maps, and tools for exploring drought across the U.S.
  • The European Commission's European Drought Observatory provides drought-related information across Europe. The site contains data-based maps of indicators, tools for visualizing and analyzing the information, and reports of specific regional droughts.
  • The GEOGLAM Global Rangelands and Pasture Productivity Map is an online geospatial tool that provides information derived from Earth observations about the state and condition of global rangelands.

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Published August 24, 2021

Page Last Updated: Aug 24, 2021 at 11:12 AM EDT