Improving terrestrial evapotranspiration estimation across China during 2000-2018 with machine learning methods

This dataset is produced by the paper (Improving terrestrial evapotranspiration estimation across China during 2000-2018 with mechine learning methods). We used the random forest to merge five process-based ET model.The data in distinct Geotiff file are named as ‘RF_yy_dd_ET.tif’, where ‘yy’ and ‘dd’ denote year, order. The order is from 1 to 36. There are 3 issues per month. Assuming there are N days in this month, the unit of the first 2 issues is mm/10day, and the unit of the third issue is mm/(N-20) day. The scale factor is 0.01.