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Estimation of actual evapotranspiration (ETa) based on reference evapotranspiration (ETo) and the crop coefficient (Kc) remains one of the most widely used ETa estimation approaches. However, its application in non-agricultural and natural environments has been limited, largely due to the lack of well-established Kc coefficients in these environments. Alternate Kc estimation approaches have thus been proposed in such instances, with techniques based on the use of leaf area index (LAI) estimates being quite popular. In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning to quantify the water use of a commercial forest plantation situated within the eastern region of South Africa. Various machine learning-based models were trained and evaluated to predict Kc as a function of LAI, with the Kc estimates derived from the best-performing model then being used in conjunction with in situ measurements of ETo to estimate ETa. The ET estimates were then evaluated through comparisons against in situ measurements. An ensemble machine learning model showed the best performance, yielding RMSE and R2 values of 0.05 and 0.68, respectively, when compared against measured Kc. Comparisons between estimated and measured ETa yielded RMSE and R2 values of 0.51 mm d−1 and 0.90, respectively. These results were quite promising and further demonstrate the potential of geospatial cloud computing and machine learning-based approaches to provide a robust and efficient means of handling large volumes of data so that they can be optimally utilised to assist planning and management decisions.
Gokool et al. (Thu,) studied this question.