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. <em>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. </em><em>https://dx.doi.org/10.1016/j.rse.2022.113184</em> <em>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. </em><em>https://dx.doi.org/10.1016/j.jag.2022.102888</em> <br> 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. <em>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. </em><em>https://dx.doi.org/10.1038/s41597-022-01522-z</em> <em>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 </em><em>http://doi.org/10.6084/m9.figshare.19352501</em> <em>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. </em><em>https://dx.doi.org/10.1016/j.agwat.2022.107458</em>