IrriMap_CN: Improved annual irrigation maps across China in 2000–2019 based on satellite imagery and machine-learning method

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. The spatial reference system of this dataset is EPSG: 4326 (WGS-1984).

Readers can refer to the following publications for more details on the methods. 

Zhang, C., Dong, J., Ge, Q., 2022. IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning. Remote Sens. Environ. https://dx.doi.org/10.1016/j.rse.2022.113184

Zhang, C., Dong, J., Xie, Y., Zhang, X., Ge, Q., 2022. Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 112, 102888. https://dx.doi.org/10.1016/j.jag.2022.102888

In addition, we also posted the link of IrriMap_Syn dataset (The 500-m irrigated cropland maps in China based on a synergy mapping method) and relevant publications as follows. The IrriMap_Syn dataset, as statistics-constraint irrigation maps, provide important ground truth data (training samples) for the generation of IrriMap_CN. 

Zhang, C., Dong, J., Ge, Q., 2022. Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products. Sci. Data 9, 407. https://dx.doi.org/10.1038/s41597-022-01522-z

Zhang, C., Dong, J., Ge, Q., 2022. The 500-m irrigated cropland maps in China during 2000-2019 based on a synergy mapping method. figshare http://doi.org/10.6084/m9.figshare.19352501

Zhang, C., Dong, J., Zuo, L., Ge, Q., 2022. Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets. Agric. Water Manage. 263, 107458-107470. https://dx.doi.org/10.1016/j.agwat.2022.107458