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Description

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30 Validation (GFSAD30VAL) data product provides a thorough and independent accuracy assessment and validation of the cropland extent products produced for each of the seven regions. The accuracy assessment and validation process utilizes a cluster of 3 by 3 pixels of 30 meter data to resample the product to 90 meter resolution. Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe.

Known Issues

  • Known issues, including constraints and limitations, are provided on page 18 of the ATBD.

Product Summary

Platforms
LANDSAT-8 ,
LANDSAT-7 ,
LANDSAT-5 ,
Sentinel-2A
Instruments
OLI ,
ETM+ ,
TM ,
Sentinel-2 MSI
Spatial Extent

N: 90 S: -90 E: 180 W: -180

Spatial Resolution
90 Meters x 90 Meters
Location
GLOBAL LAND
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
2008-01-01 to 2016-12-31
Temporal Resolution
9 Year
Data Partner
Land Processes Distributed Active Archive Center (LP DAAC) ,
Western Geographic Science Center, U.S. Geological Survey, U.S. Department of the Interior (DOI/USGS/WGSC)
Concept ID
C2763261723-LPCLOUD
Data State
COMPLETE
Number of Files/Granules
1
Processing Level
3
Published
Updated
Science Keywords
Crop/Plant Yields ,
Land Use Classes ,
Landscape Patterns ,
Cropland

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

Congalton, R., Yadav, K., McDonnell, K., Poehnelt, J., Stevens, B., Gumma, M., Teluguntla, P., & Thenkabail, P. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Validation Global 30 m V001 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/GFSAD/GFSAD30VAL.001 Date Accessed: 2026-05-03

File Naming Convention

GFSAD30SEACE_2015_S20W160_001_2018080123000.tif

File name begins with Product Short Name (GFSAD30SEACE) followed by the Year of Acquisition (2015), the latitude and longitude of the lower left corner of the tile (S20W160), Version (001), Julian Date and Time of Processing as YYYYDDDHHMMSS (2018080123000), and the Data Format (tif).

Documents

Publications Citing This Dataset

Filters
Title Year Author Topic
More than three-quarters of environmental migration in Somalia is driven by water deficiency for food and livestock production Wolde, Sinafekesh Girma, DOdorico, Paolo, Rulli, Maria Cristina Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Novel Spatiotemporal ConvLSTM-Based Cellular Automata Model for Zhou, Ye, Qiu, Yu, Wu, Tao, Lv, Laishui Crop/Plant Yields, Landscape Patterns, Cropland, Land Use Classes, Vegetation Cover, Reflectance
Landsat-Derived Rainfed and Irrigated-Area Product for Conterminous United States for the Year 2020 (LRIP30 CONUS 2020) Using Supervised and Unsupervised Machine Learning on the Cloud Teluguntla, Pardhasaradhi, Thenkabail, Prasad S., Oliphant, Adam, Aneece, Itiya, Biggs, Trent, Gumma, Murali Krishna, Foley, Daniel, McCormick, Richard, Neelam, Rohitha, Long, Emerson, Lawton, Jake Crop/Plant Yields, Landscape Patterns, Cropland, Land Use Classes, Vegetation Cover, Reflectance
Agricultural plastics as marine pollutants: Empirical evidence from inland and coastal field surveys Morales-Caselles, Carmen, Viejo, Josue, Montero, Enrique, Cozar, Andres Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Landsat image classification using a deep learning model and Sun, Zhelun, Li, Xuecao, Wei, Hong, Feng, Zemin, Yang, Jun Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent productsDownload Analysis Oliphant, Adam, Thenkabail, Prasad, Teluguntla, Pardhasaradhi Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland, Vegetation Cover
Finer-resolution mapping of global land cover: Recent developments, consistency analysis, and prospects Liu, Liangyun, Zhang, Xiao, Gao, Yuan, Chen, Xidong, Shuai, Xie, Mi, Jun Human Settlements, Urbanization/Urban Sprawl, Infrastructure, Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud Thenkabail, Prasad S., Teluguntla, Pardhasaradhi G., Xiong, Jun, Oliphant, Adam, Congalton, Russell G., Ozdogan, Mutlu, Gumma, Murali Krishna, Tilton, James C., Giri, Chandra, Milesi, Cristina, Phalke, Aparna, Massey, Richard, Yadav, Kamini, Sankey, Temuulen, Zhong, Ying, Aneece, Itiya, Foley, Daniel Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Vegetation Cover
Land-use change and the livestock revolution increase the risk of zoonotic coronavirus transmission from rhinolophid bats Rulli, Maria Cristina, DOdorico, Paolo, Galli, Nikolas, Hayman, David T. S. Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
A review of satellite-based global agricultural monitoring systems available for Africa Nakalembe, Catherine, Becker-Reshef, Inbal, Bonifacio, Rogerio, Hu, Guangxiao, Humber, Michael Laurence, Justice, Christina Jade, Keniston, John, Mwangi, Kenneth, Rembold, Felix, Shukla, Shraddhanand, Urbano, Ferdinando, Whitcraft, Alyssa Kathleen, Li, Yanyun, Zappacosta, Mario, Jarvis, Ian, Sanchez, Antonio Land Use/Land Cover Classification, Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Large climate mitigation potential from adding trees to agricultural Chapman, Melissa, Walker, Wayne S., CookPatton, Susan C., Ellis, Peter W., Farina, Mary, Griscom, Bronson W., Baccini, Alessandro Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the ... Gumma, Murali Krishna, Thenkabail, Prasad S., Teluguntla, Pardhasaradhi G., Oliphant, Adam, Xiong, Jun, Giri, Chandra, Pyla, Vineetha, Dixit, Sreenath, Whitbread, Anthony M Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth ... Oliphant, Adam J., Thenkabail, Prasad S., Teluguntla, Pardhasaradhi, Xiong, Jun, Gumma, Murali Krishna, Congalton, Russell G., Yadav, Kamini Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland, RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps
A review of global-local-global linkages in economic land-use/cover Hertel, Thomas W, West, Thales A P, Borner, Jan, Villoria, Nelson B Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Spatial global assessment of the pest Bagrada hilaris (Burmeister) Carvajal, Mario A, Alaniz, Alberto J, NunezHidalgo, Ignacio, GonzalezCesped, Carlos Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud ... Teluguntla, Pardhasaradhi, Thenkabail, Prasad S, Oliphant, Adam, Xiong, Jun, Gumma, Murali Krishna, Congalton, Russell G., Yadav, Kamini, Huete, Alfredo Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Liu, Jianhong, Zhu, Wenquan, Atzberger, Clement, Zhao, Anzhou, Pan, Yaozhong, Huang, Xin Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
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Variables

The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.

For questions on a specific variable, please use the Earthdata Forum.

Name Description Units Data Type Fill Value Valid Range Scale Factor Offset
SHP Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe. Class OTHER N/A N/A N/A N/A
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