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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 over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). 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 GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.

Known Issues

  • Known issues, including constraints and limitations, are provided on page 20 of the ATBD.

Product Summary

Platforms
LANDSAT-8 ,
LANDSAT-7 ,
DEM ,
LANDSAT-5
Instruments
OLI ,
ETM+ ,
TM
Spatial Extent

N: 90 S: 0 E: 180 W: -180

Spatial Resolution
30 Meters x 30 Meters
Location
EUROPE ,
MIDDLE EAST ,
RUSSIAN FEDERATION ,
SOUTHCENTRAL ASIA
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
2013-01-01 to 2016-12-31
Temporal Resolution
4 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
C2763261648-LPCLOUD
Data State
COMPLETE
Number of Files/Granules
143
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

Phalke, A., Ozdogan, M., Thenkabail, P., Congalton, R., Yadav, K., Massey, R., Teluguntla, P., Poehnelt, J., & Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East 30 m V001 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/GFSAD/GFSAD30EUCEARUMECE.001 Date Accessed: 2026-05-05

File Naming Convention

GFSAD30EUCEARUMECE_2015_N40E90_001_2016272130000.tif

File name begins with Product Short Name (GFSAD30EUCEARUMECE) followed by the Year of Acquisition (2015), the latitude and longitude of the lower left corner of the tile (N40E90), Version (001), Julian Date and Time of Processing as YYYYDDDHHMMSS (2016272130000), and the Data Format (tif).

Documents

Publications Citing This Dataset

Filters
Title Year Author Topic
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
SMALLHOLDERS PERCEPTIONS OF CLIMATE CHANGE IN A POST-SOCIALIST ALBANIA Lippi, Simona, Sanfilippo, Massimiliano Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Toward understanding land use land cover changes and their effects on land surface temperature in yam production area, Cote d'Ivoire, Gontougo Region, using ... Aka, Kadio S. R., Akpavi, Semihinva, Dibi, NDa Hyppolite, Kabo-Bah, Amos T., Gyilbag, Amatus, Boamah, Edward Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region Li, Danya, Gajardo, Joaquin, Volpi, Michele, Defraeye, Thijs 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
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
Complex life histories predispose aphids to recent abundance declines Crossley, Michael S., Smith, Olivia M., Davis, Thomas S., Eigenbrode, Sanford D., Hartman, Glen L., LagosKutz, Doris, Halbert, Susan E., Voegtlin, David J., Moran, Matthew D., Snyder, William E. Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Croplands intensify regional and global warming according to satellite observations Zhou, Decheng, Xiao, Jingfeng, Frolking, Steve, Liu, Shuguang, Zhang, Liangxia, Cui, Yaoping, Zhou, Guoyi Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland, Vegetation Cover
A new framework to map fine resolution cropping intensity across the globeAlgorithm, validation, and implication Liu, Chong, Zhang, Qi, Tao, Shiqi, Qi, Jiaguo, Ding, Mingjun, Guan, Qihui, Wu, Bingfang, Zhang, Miao, Nabil, Mohsen, Tian, Fuyou, Zeng, Hongwei, Zhang, Ning, Bavuudorj, Ganbat, Rukundo, Emmanuel, Liu, Wenjun, Bofana, Jose, Beyene, Awetahegn Niguse, Elnashar, Abdelrazek Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland, Plant Phenology
Mapping croplands of Europe, middle east, russia, and central asia using landsat, random forest, and google earth engine Phalke, Aparna R., Ozdogan, Mutlu, Thenkabail, Prasad S., Erickson, Tyler, Gorelick, Noel, Yadav, Kamini, Congalton, Russell G. Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network Waldner, Francois, Diakogiannis, Foivos I. Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Conflation of expert and crowd reference data to validate global binary thematic maps Waldner, Francois, Schucknecht, Anne, Lesiv, Myroslava, Gallego, Javier, See, Linda, Perez-Hoyos, Ana, d'Andrimont, Raphael, de Maet, Thomas, Bayas, Juan Carlos Laso, Fritz, Steffen, Leo, Olivier, Kerdiles, Herve, Diez, Monica, Van Tricht, Kristof, Gilliams, Sven, Shelestov, Andrii, Lavreniuk, Mykola, Simoes, Margareth, Ferraz, Rodrigo, Bellon, Beatriz, Begue, Agnes, Hazeu, Gerard, Stonacek, Vaclav, Kolomaznik, Jan, Misurec, Jan, Veron, Santiago R., de Abelleyra, Diego, Plotnikov, Dmitry, Mingyong, Li, Singha, Mrinal, Patil, Prashant, Zhang, Miao, Defourny, Pierre 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 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
Band 1 Cropland Extent for Europe, Central Asia, Russia, and the Middle East defined with three classes N/A uint8 N/A 0 to 2 N/A N/A
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