Use of machine learning to improve the robustness of spatial estimation of evapotranspiration

Estimation of the crop water requirement is critical in the optimization of the agricultural production process, due to that yield and costs are directly affected by this estimation. Nowadays, remote sensing is a useful tool for estimating Evapotranspiration (ET), since it is possible to map their spatial and temporal variability. ET models using satellite images have been developed in the last decades, using in most cases the surface energy balance which has generated good ET representation in different study sites. One of these models is METRIC (Mapping EvapoTranspiration at high Resolution using Internalized Calibration), which estimates ET using mainly data from Landsat 7 and 5 images, and a physical-empirical basis to solve the surface energy balance. The main drawback of the METRIC model is the low robustness in the selection of two parameters called anchor pixels. Even though the rules to select anchor pixels are standardized, the procedure requires a user to choose the area where these pixels will be selected. In this sense, ET estimation is highly sensible to this selection, producing important differences when different anchor pixels are selected. In this study, a machine learning method is implemented through the GEOBIA (Geographic Object Based Image Analysis) approach for the identification of anchor objects, changing the focus from the pixels to the objects. Image segmentation and classification processes are used for an adequate selection of anchor objects, considering spectral and contextual information. The main contribution of this work proves that it is not necessary to choose an area to select the anchor parameters, improving the numerical stability of the model METRIC and increasing the robustness of the ET estimation. Results were validated by comparing the original selection of anchor pixels, as well as in-situ ET estimation using data obtained from Surface Renewal Stations, in sugar beat crop