# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 5 Conclusions  
Approach 2 had statistical results superior to approach 1, mainly for the XgbLinear model, which obtained R\({}^{2}\) of 0.91 and RMSE of 0.10, whereas the metrics for the same model considering approach 1 were 0.80 and 0.15 for the R\({}^{2}\) and RMSE, respectively. This result was mainly influenced by the greater number of spectral bands that are strongly related to water content as the short infrared and the red edge, thus being the model to be applied. However, all models developed showed limitations when the dependent variable presents values lower than 0.6, a condition in which the crop canopy has not completely covered the soil, and there is greater variability. Despite the limitation when the soil is not fully covered, the application proved efficient in predicting ET\({}_{\mathrm{r}}F\) since the analysis had values close to zero and the maximum distance only in the linear regression algorithms for both approach 1 and approach 2. Furthermore, it was possible to note that ET\({}_{\mathrm{r}}F\) cannot be considered the same as K\({}_{\mathrm{c}}\) because the onboard sensors capture the actual condition of the crop in the field, hydric comfort or not. Hence, ET\({}_{\mathrm{r}}F\) can be understood as the product between K\({}_{\mathrm{c}}\) and K\({}_{\mathrm{s}}\) that best represents field conditions. The use of the MSI sensor and machine learning techniques proved to be a new and simple alternative to estimate ET\({}_{\mathrm{r}}F\) through spectral information, complementing the METRIC model estimation using OLI and TIRS sensors and increasing the frequency that information is generated for areas of interest. The combination of these sensors is useful to obtain the highest temporal resolution of the crop, especially for irrigated agriculture, which requires K\({}_{\mathrm{c}}\) and K\({}_{\mathrm{s}}\) coefficients to be determined daily for adequate replenishment of the irrigation blade. Finally, this work highlights the possibility of using this methodology for other remote sensors to calculate spatial and temporal evapotranspiration, enriching the scientific debate. Further, it can be applied in other locations and different cultures since the data needed are from the orbital sensors and meteorological data from the area of interest. Thus, the methodology, despite being extensive, is easy to apply with a low financial cost. However, in hopes of advancements, this methodology can be performed with field instrumentation to collect the actual evapotranspiration, as this evapotranspiration would be replaced by the evapotranspiration of the METRIC model, but more funding would be required. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/atmos13091518/s1](https://www.mdpi.com/article/10.3390/atmos13091518/s1)]([https://www.mdpi.com/article/10.3390/atmos13091518/s1](https://www.mdpi.com/article/10.3390/atmos13091518/s1)), Figure S1: Importance of the variables for the approach 1 algorithm; Figure S2: Importance of the variables for the approach 1 Cubist algorithm; Figure S3: Importance of the variables for the approach 1 XgbLinear algorithm; Figure S4: Importance of the variables for the approach 1 XgbTree algorithm; Figure S5: Importance of the variables for the approach 2 LM algorithm; Figure S6: Importance of the variables for the approach 2 Cubist algorithm; Figure S7: Importance of the variables for the approach 2 XgbLinear algorithm; Figure S8: Importance of the variables for the approach 2 XgbTree algorithm. Conceptualization, R.A.d.S., E.I.F.-F. and E.C.M. ; methodology, R.A.d.S., R.F., E.I.F.-F., E.C.M. and R.D.S.L. ; software, R.A.d.S. ; validation, R.A.d.S., R.F., E.I.F.-F. and E.C.M. ; formal analysis, R.A.d.S., E.I.F.-F. and E.C.M. ; investigation, R.A.d.S. ; resources, E.C.M. ; data curation, R.A.d.S. ; writing--original draft preparation, R.A.d.S. ; writing--review and editing, R.A.d.S., R.F., R.D.S.L., V.B.B. and C.M.U.N. ; visualization, E.I.F.-F., R.F., R.D.S.L., V.B.B. and E.C.M. ; supervision, E.C.M.