Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine

作者：Chao Zhang, Jinwei Dong, Yanhua Xie, Xuezhen Zhang, Quansheng Ge · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2022 · DOI：10.1016/j.jag.2022.102888 · 被引用次数：51 · 研究领域：Remote Sensing in Agriculture、Flood Risk Assessment and Management、Land Use and Ecosystem Services

Agricultural irrigation is an important vehicle for increasing crop yield, but large-scale irrigation has posed great challenges to global and regional water availability and climate change via altering land–atmosphere interactions. The knowledge of irrigation distribution is essential to understand regional water cycles and guide agricultural management decision-making, but such information is scarce in China. We developed a remote sensing-dominated framework to map irrigated croplands in China at 500 m resolution using a synergetic training sample generating method, machine learning classifier, and a cloud computing platform (Google Earth Engine, GEE). To overcome the challenges of lacking nationwide training samples, we first produced two provisional irrigation maps by fusing statistics and MODIS-derived annual peak greenness indices. The two provisional irrigation maps were then spatially filtered with an existing irrigation product (GRIPC) to construct the initial training sample pool. Next, to enhance the robustness and cover more irrigated candidates, we screened and introduced the irrigated croplands in three land use/cover maps (CCI-LC, GLC_FCS, and NLCD) to supplement the training data pool. Afterward, we utilized locally adaptive random forest classifiers and data cubes (MODIS-derived spectral indices, climatic and topographic variables) to generate irrigation maps in each province of China on GEE. The resulting map outperformed other current irrigation maps with a...