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Global Food Security-support Analysis Data 30 meter (GFSAD30)

Jul 25, 2023
Dataset extended description

The Land Processes Distributed Active Archive Center (LP DAAC) is pleased to announce the availability of the The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data 30 meter (GFSAD30) Cropland Extent data product.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 collection provides cropland extent data across the globe, divided and distributed into 7 separate regional datasets, for nominal year 2015 (2010 for North America) at 30 meter resolution. Additionally, the validation dataset used to conduct an independent accuracy assessment of global cropland extent is available.

The Digital Object Identifier (DOI) for each dataset is given below to provide users with a persistent link to the product information.

Dataset keywords
Dataset last access
Dataset JRC path
land/lpdaac/ref
Dataset type
Dataset category
Dataset format
tif zip
Dataset availability
NOT ACTUALLY AVAILABLE
Dataset temporal frequency
none
Dataset spatial extent
Dataset spatial resolution description
About 30 m
Dataset region of interest
Dataset JRC contact
cesar.carmona-moreno@ec.europa.eu
Dataset credits

Linda See, Dmitry Schepaschenko, Myroslava Lesiv, Ian McCallum, Steffen Fritz, Alexis Comber, Christoph Perger, Christian Schill, Yuanyuan Zhao, Victor Maus, Muhammad Athar Siraj, Franziska Albrecht, Anna Cipriani, Mar’yana Vakolyuk, Alfredo Garcia, Ahmed H. Rabia, Kuleswar Singha, Abel Alan Marcarini, Teja Kattenborn, Rubul Hazarika, Maria Schepaschenko, Marijn van der Velde, Florian Kraxner, Michael Obersteiner, Building a hybrid land cover map with crowdsourcing and geographically weighted regression, In ISPRS Journal of Photogrammetry and Remote Sensing, Volume 103, 2015, Pages 48-56, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2014.06.016.
(http://www.sciencedirect.com/science/article/pii/S0924271614001713)
Keywords: Land cover; Validation; Crowdsourcing; Map integration; Global land cover; Geographically weighted regression

Dataset disclaimer

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