Mathematical modelling of evapotranspiration by using remote sensing and data mining

Abstract

Precise evaluation of evapotranspiration in an extended area is crucial for water requirement. By using remote sensing evapotranspiration algorithms, many climatological variables are needed. In case of using climatological variable measurements, many climatic stations must be established in that specific area. By using data mining method integrated with remote sensing, evapotranspiration can be calculated with high accuracy. A physical-based SEBAL evapotranspiration algorithm was modeled by GIS model builder for ET calculations. Albedo, emissivity, and Normalized Difference Water Index (NDWI) were considered as M5 decision tree model inputs. Evapotranspiration was evaluated for 3 April 2020 to 17 September 2020 and the equations were extracted in the M5 decision tree model and these equations were modeled in GIS by using python scripts for 3 April 2020 to 17 September 2020. The results make clear that the mathematical decision tree model can estimate the evapotranspiration gained by physical-based SEBAL algorithm in high accurately.