Data
[Data] 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
- Download URL: http://doi.org/10.6084/m9.figshare.20363115
- Data format: .tif
- Pixel size: 500 m
- Projection: EPSG: 4326 (WGS84)
- Values: 1 denotes irrigated and 0 denotes non-irrigated
Code and dataset for “Characterizing spatial, diurnal, and seasonal patterns of agricultural irrigation expansion-induced cooling in Northwest China from 2000 to 2020”
Here we provide the code and dataset to reproduce the results of the following paper:
Zhang, C., Ge, Q., Dong, J., Zhang, X., Li, Y., Han, S., 2023. Characterizing spatial, diurnal, and seasonal patterns of agricultural irrigation expansion-induced cooling in Northwest China from 2000 to 2020. Agricultural and Forest Meteorology. 109304. https://doi.org/10.1016/j.agrformet.2022.109304
Download URL: https://doi.org/10.6084/m9.figshare.21805167