# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 3 Results  
### Models  
In approach 1, 11 predictors were generated by normalizing the spectral bands of spatial resolution of 10 m. Only seven predictors remained after removing predictors with a correlation above 95% and five when using the recursive feature elimination (RFE). Figure 4 shows the results after the application of the RFE algorithm. Notably, the potential number of predictor variables (ideas) is five. Thus, in the case of more than five variables, the ones with low importance can be removed without significantly impacting the trained models. On the other hand, less than five variables negatively affect the model prediction. In approach 2, the number of predictors generated by normalizing the bands is 45, higher than in approach 1 because of the bigger number of spectral bands for the special resolution of 20 m. When establishing the baseline of 95% of correlation, 22 variables remained and were later reduced to 12 after the application of the RFE. Figure 5 shows the stability of the statistical metrics using 12 predictable variables to train the model. Figure 4: Statistical results for the selection of predictor variables when applying the RFE in approach 1. Figure 5: Statistical results for the selection of predictor variables when applying the RFE in approach 2. In Table 4, the 5 predictor variables selected for approach 1 and the 12 selected for approach 2 can be found. It is worth mentioning that the predictors in this table are not rated by importance, as each algorithm assigns different importance after the training. In supplementary materials are the figures with the importance of the predictors for each model in approaches 1 and 2. For approach 1, the most common wavelengths among the predictors were B2 (blue) and B4 (red), both appearing twice, counted individually and in the NRPB, followed by B3 (green) and finally B8 (near infrared). B2 and B4, which stood out in this approach, are absorbed in higher intensity by plants from photosynthesizing pigments [11]. B3 is also absorbed by the same pigments, however, at a much lower intensity. While B8 tends to be reflected or transmitted because when there is absorption, the physiological structures of the plant heat up, and the plant tends to dissipate this energy through its structures. Therefore, the more nourished the dissipation structures are, the more efficient the reflectance and/or transmittance are. Thus, for approach 1, the wavelengths B2, B3, and B4 correspond to the pigments of the leaves, while B8 (Near-infrared) is related to the structural part. As a result, the selected predictors are important for both the photosynthesis process and the structure of the cellular components of the plants studied. Hence, three wavelengths have characteristics related to the photosynthesizing pigments and one related to the structure. In approach 2, the bands that stood out were B12 (short infrared), appearing five times, and B2, appearing four times. B12 is a short wavelength related to water content, which is an object of study in this work, through the physical-biological process of evapotranspiration. B5 and B6 bands, both red edges, also stood out, representing a transition among the visible region and near-infrared, and B8 and B11, shortwave infrared. Thus, it can be noted in this approach that the selected predictors are related not only to the photosynthetic and structural parts but also to water content. Hence, it can be inferred that three wavelengths with photosynthetic characteristics were assigned, one structural, two in the transition from photosynthetic to structural, and two with water content. Test performances for approach 1 for each machine learning model can be seen in Figure 6.