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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 Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). 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 GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE 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

  • Certain small islands in the Pacific are not classified and therefore data for these areas are not provided.
  • Additional known issues, including constraints and limitations, are provided on page 19 of the ATBD.

Product Summary

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

N: 60 S: -45 E: 180 W: 70

Spatial Resolution
30 Meters x 30 Meters
Location
EASTERN ASIA ,
SOUTHEASTERN 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
C2763261715-LPCLOUD
Data State
COMPLETE
Number of Files/Granules
54
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

Oliphant, A., Thenkabail, P., Teluguntla, P., Xiong, J., Congalton, R., Yadav, K., Massey, R., Gumma, M., & 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 Southeast Asia 30 m V001 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/GFSAD/GFSAD30SEACE.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
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
Predicting conservation priority areas in Borneo for the critically endangered helmeted hornbill (Rhinoplax vigil) Hatten, C.E.R., Hadiprakarsa, Y.Y., Lee, C.K.F., Jain, A., Kaur, R., Miller, A., Cheema, S., Au, N.J., Khalid, S., Dingle, C. Land Use/Land Cover Classification, 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
Assessing the influence of land use/land cover alteration on climate variabilityAn analysis in the Aurangabad District of Maharashtra state, India Masroor, Md, Avtar, Ram, Sajjad, Haroon, Choudhari, Pandurang, Kulimushi, Luc Cimusa, Khedher, Khaled Mohamed, Komolafe, Akinola Adesuji, Yunus, Ali P., Sahu, Netrananda Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Strengthening climate-resilient development and transformation in Viet Nam Rana, Arun, Zhu, Qinhan, Detken, Annette, Whalley, Karina, Castet, Christelle 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
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
Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover HoffmanHall, Amanda, Puett, Robin, Silva, Julie A., Chen, Dong, Baer, Allison, Han, Kay Thwe, Han, Zay Yar, Thi, Aung, Htay, Thura, Thein, Zaw Win, Aung, Poe Poe, Plowe, Christopher V., Nyunt, Myaing Myaing, Loboda, Tatiana V. Urban Lands, Land Use/Land Cover, Urbanization/Urban Sprawl, Infrastructure, RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Crop/Plant Yields, Land Use Classes, Landscape Patterns, Cropland
Large-scale crop mapping from multisource remote sensing images in Google Earth Engine Liu, Xinkai, Zhai, Han, Shen, Yonglin, Lou, Benke, Jiang, Changmin, Li, Tianqi, Hussain, Sayed Bilal, Shen, Guoling Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), 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
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 Southeast and Northeast Asia defined with three classes N/A uint8 N/A 0 to 2 N/A N/A
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