IrriMap_CN: Annual irrigation maps across China in 2000-2019 based on satellite observations, environmental variables, and machine learning
文献类型：期刊论文
| 作者 | Zhang, Chao1,2; Dong, Jinwei1; Ge, Quansheng1 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 280页码:14 |
| 关键词 | Irrigation China MODIS Random forest Google Earth Engine Spatiotemporal dynamics |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2022.113184 |
| 英文摘要 | Agricultural irrigation, as an important practice to protect crops from drought and promote grain yield, has a long history in China. A timely and precise dataset about the extent and dynamics of irrigated areas is necessary for water allocation and agricultural management but is scarce in China. Here we developed annual irrigated cropland maps across China (IrriMap_CN) at 500-m resolution from 2000 to 2019, using MODIS data, machine-learning method, and Google Earth Engine platform. First, we generated annual nationwide training samples by strictly screening the existing irrigation maps downscaled from the statistical data. Second, we implemented locally adaptive random forest classifiers in 511 nominal 1 x 1 grid cells across China with MODIS vegetation indices, climatic factors, and topography variables. Third, we conducted nationwide pixel-wise validation of the IrriMap_CN using independent samples. The validation results based on more than 3000 ground truth points revealed that IrriMap_CN had high accuracies ranging from 77.2% to 85.9%. The time series of IrriMap_CN detected substantial expansion of irrigated areas in Xinjiang and Heilongjiang (more than 50% in total) and pronounced decreases in Sichuan, Jiangsu, and Hebei. The analyses of irrigation frequency, start time, and end time implied that North China Plain was the most intensive irrigated area; but the irrigation area showed a decreasing trend since 2000, consistent with the reduced agricultural water consumption. The annual irrigation datasets allow us to understand the spatiotemporal dynamics of irrigated croplands in China and are expected to contribute to the improvement of earth system models and facilitate sustainable agricultural water management. |
| WOS关键词 | LAND-COVER CLASSIFICATION ; WINTER-WHEAT ABANDONMENT ; HAIHE RIVER-BASIN ; RANDOM FOREST ; TIME-SERIES ; PADDY RICE ; MULTITEMPORAL MODIS ; CROPLAND EXTENT ; PLANTING AREA ; PLAIN |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040301] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100400] ; National Natural Science Foundation of China[41871349] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000860480000001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/185038]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chao,Dong, Jinwei,Ge, Quansheng. IrriMap_CN: Annual irrigation maps across China in 2000-2019 based on satellite observations, environmental variables, and machine learning[J]. REMOTE SENSING OF ENVIRONMENT,2022,280:14. |
| APA | Zhang, Chao,Dong, Jinwei,&Ge, Quansheng.(2022).IrriMap_CN: Annual irrigation maps across China in 2000-2019 based on satellite observations, environmental variables, and machine learning.REMOTE SENSING OF ENVIRONMENT,280,14. |
| MLA | Zhang, Chao,et al."IrriMap_CN: Annual irrigation maps across China in 2000-2019 based on satellite observations, environmental variables, and machine learning".REMOTE SENSING OF ENVIRONMENT 280(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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