Improving terrestrial evapotranspiration estimation across China during 2000-2018 with machine learning methods
收藏DataCite Commons2020-08-25 更新2024-07-28 收录
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https://figshare.com/articles/Improving_terrestrial_evapotranspiration_estimation_across_China_during_2000-2018_with_machine_learning_methods/12278684/1
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资源简介：
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.
提供机构：
figshare
创建时间：
2020-06-15
