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

文献类型：期刊论文

作者Zhang, Chao1,2; Dong, Jinwei1; Xie, Yanhua3,4; Zhang, Xuezhen1; Ge, Quansheng1
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2022-08-01
卷号112页码:13
关键词Irrigation China Training sample pool MODIS Google Earth Engine Random forest
ISSN号1569-8432
DOI10.1016/j.jag.2022.102888
通讯作者Dong, Jinwei(dongjw@igsnrr.ac.cn) ; Ge, Quansheng(geqs@igsnrr.ac.cn)
英文摘要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.
WOS关键词PADDY RICE ; TIME-SERIES ; LAND ; TEMPERATURE ; MODIS ; WATER ; AREA ; PATTERNS ; PLAIN ; STATISTICS
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Remote Sensing
语种英语
WOS记录号WOS:000844311700002
出版者ELSEVIER
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/311030/166600]  
专题中国科学院地理科学与资源研究所
通讯作者Dong, Jinwei; Ge, Quansheng
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
3.Univ Wisconsin Madison, Nelson Inst Ctr Sustainabil & Global Environm SAGE, 1710 Univ Ave, Madison, WI 53726 USA
4.Univ Wisconsin Madison, DOE Great Lakes Bioenergy Res Ctr, Madison, WI 53726 USA
推荐引用方式
GB/T 7714		 Zhang, Chao,Dong, Jinwei,Xie, Yanhua,et al. Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,112:13.
APA Zhang, Chao,Dong, Jinwei,Xie, Yanhua,Zhang, Xuezhen,&Ge, Quansheng.(2022).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,112,13.
MLA Zhang, Chao,et al."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 112(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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