Evaluating evapotranspiration using data mining instead of physical-based model in remote sensing

Precise calculations for determining the water requirements of plants and the extent of evapotranspiration are crucial in determining the volume of water consumed for plant production. In order to estimate evapotranspiration over an extended area, different remote sensing algorithms require numerous climatological variables; however, climatological variable measurements cover only limited areas thus resulting into erroneous calculations over extended areas. The exploiting of both data mining and remote sensing technologies allows for the modeling of the evapotranspiration process. In this research, the physical-based SEBAL evapotranspiration algorithm was remodeled using M5 decision tree equations in GIS. The input variables of the M5 decision tree consisted of Albedo, emissivity, and Normalized Difference Water Index (NDWI) which were defined as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS using python scripts for 8 April 2019 and 3 April 2020, respectively. The calculated correlation coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42, respectively, and for 3 April 2020 were 0.95, 0.31, and 0.23 in order. Moreover, for the further evaluation of the model, a sensitivity analysis and an uncertainty analysis were carried out. The analysis revealed that evapotranspiration is more sensitive to Albedo than the two other model inputs, and when applying data mining techniques instead of SEBAL, the estimation of evapotranspiration has a lower accuracy.