期刊
REMOTE SENSING OF ENVIRONMENT
卷 219, 期 -, 页码 162-179出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2018.10.013
关键词
Google Earth Engine; Random Forest; Landsat; RHSeg; Cluster computing; Object-based analysis; North American croplands
资金
- National Aeronautics and Space Administration (NASA) through its MEaSUREs (Making Earth System Data Records for Use in Research Environments) initiative [NNH13AV82I]
- U.S. Geological Survey [G13C00129]
Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental -scale cropland extent map for North America at 30 m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are > 90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Informacion Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R-2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.
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