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 an overall accuracy of 79.2% . The map also showed a reasonable consistency with statistical data at the province and prefecture levels, with the determination coefficient (R2) of 0.89 and 0.77, respectively. In total, we identified 87.04 million hectares of irrigated croplands in mainland China in 2015. Using the resulting map and water use statistics, we found a high correlation between irrigation area and agricultural water use in Northwest, Northeast, and South China, and a low correlation in North China Plain. This map is expected to serve national water resource management and assist decision-making in improving agricultural adaption to climate change.
Zhang, C; Dong, JW; Xie, YH; Zhang, XZ; Ge, QS
Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine
International Journal Of Applied Earth Observation And Geoinformation