Global irrigation mapping
\cite{Siebert_2005} based on FAO census data
\cite{Thenkabail_2009} the GIAM dataset
\cite{Portmann_2010} about 10 km resolution, for 26 crops, at monthly-scale. census data-based
\cite{Salmon_2015} MODIS RS+census
\cite{Siebert_2015} Historical data from 1900 to 2015.
\cite{Meier_2018} This data relies on difference between remote sensing variables and reanalysis. coarse resolution at 0.25 deg.
National-level
USA
\cite{Xie_2019}
Use landsat and MODIS data, 30 m resolution for 2012. random forests on GEE.
China
\cite{Zhu_2014}
India
\cite{Dheeravath_2010}
Regional level
\cite{Gao_2018} 在欧洲一个小尺度20km范围内,用哨兵进行了灌溉识别
\cite{Jin_2016} Study region in 山西中南部, data source is Chinese HJ-1A/B (HuanJing(HJ) in 2010. based on peak NDVI and TNI. Too simple. Don't even mention how much training sample it used.