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Effect of the net radiation proxies on maize and soya evapotranspiration estimation using machine learning methods.
Accurate evapotranspiration (ET) estimation is essential for water management in crops, but it is not an easy task. Empirical ET methodologies require precise net radiation (Rn) measurements to obtain accurate results. Nevertheless, Rn measurements are not easy to obtain from meteorological stations...
Accurate evapotranspiration (ET) estimation is essential for water management in crops, but it is not an easy task. Empirical ET methodologies require precise net radiation (Rn) measurements to obtain accurate results. Nevertheless, Rn measurements are not easy to obtain from meteorological stations. Thus, this study explored the use of machine learning algorithms with two Rn substitutes, to estimate daily ET: the extraterrestrial solar radiation (Ra) and a modelled Rn (RnM). Support Vector Machine (SVM), Kernel Ridge (KR), Decision Tree (DT), Adaptive Boosting (AB), and Multilayer Perceptron (MLP) were applied to model FLUXNET Rn and ET observations. Adaptive Boosting produced the best field Rn measurements (RnO), yielding a Root Mean Square Error of about 16 % of the mean observed Rn. The resulting Rn (AB RnM) was used to model daily crops ET employing the above-mentioned machine learning methods with RnO, AB RnM, and Ra, in conjunction with meteorological variables and the NDVI index. The evaluated methods were suitable to estimate ET, yielding similar errors to those obtained with RnO, when contrasted with ET observations. These results demonstrate that AB and KR are applicable with rutinary meteorological and satellite data to estimate ET.
