## 2 Materials and Methods
### Statistical Data Analysis  
The following statistical variables have been considered to compare the performance of the different ET\({}_{\mathrm{a}}\) models across the spaceborne and airborne RS platforms: mean bias error (MBE), root mean square error (RMSE), normalized MBE (NMBE), normalized RMSE (NRMSE), and the coefficient of determination (R\({}^{2}\)). Equations (31)-(34) indicate MBE, NMBE, RMSE, and NRMSE, respectively:  
\[MBE=\left(\frac{1}{n}\right)\sum_{i=1}^{n}(E_{i}-O_{i}) \tag{31}\]  
\[NMBE=\left(\frac{MBE}{\overline{O}}\right)\times 100\% \tag{32}\]  
\[RMSE=\sqrt{\left(\frac{1}{n}\right)\sum_{i=1}^{n}(E_{i}-O_{i})^{2}} \tag{33}\]  
\[NRMSE=\left(\frac{RMSE}{\overline{O}}\right)\times 100\% \tag{34}\]  
where \(\overline{O}\) is the mean of the observed data; \(n\) is the sample size; E\({}_{\mathrm{i}}\) and O\({}_{\mathrm{i}}\) are the estimated and observed values, respectively. NMBE and NRMSE are given in percentages, whileEquations (32) and (34) provide statistical indicators with the same units of the primary variables. Based on the guidelines in [64], the performances of the ET\({}_{\text{a}}\) models have been classified into one of the following categories: excellent (NRMSE \(\leq\) 10%), good (10% \(\times\) NRMSE \(\leq\) 20%), fair (20% \(<\) NRMSE \(\leq\) 30%), and poor (NRMSE \(>\) 30%).  
The R\({}^{2}\), in the context of model performance assessment, informs about the degree of variability in the observed data explained by the modeling approach. Equation (35) gives the mathematical expression for R\({}^{2}\):  
\[\text{R}^{2}=\frac{\sum\left(\text{E}_{\text{i}}-\overline{\text{E}}\right) \left(\text{O}_{\text{i}}-\overline{\text{O}}\right)}{\sqrt{\left[\sum\left( \text{E}_{\text{i}}-\overline{\text{E}}\right)^{2}\right]\left[\sum\left(\text {O}_{\text{i}}-\overline{\text{O}}\right)^{2}\right]}} \tag{35}\]  
where \(\overline{E}\) is the mean value of the predictions. This study defines the optimal remote sensing platform as the source of multispectral data with the smallest NRMSE. In case two or more platforms have identical NRMSE, the highest d\({}_{\text{r}}\) index between the two platforms is considered the optimal data for a given remote sensing of the ET\({}_{\text{a}}\) algorithm. The modified index of agreement (d\({}_{\text{r}}\)) was calculated to assess model performance by comparing the sum of the residuals to the total difference between observed values and the respective mean of the measured data. Higher d\({}_{\text{r}}\) values indicate that the predicted values have more statistical agreement with the observed data, showing better model performance [65].  
Outliers have been excluded from the analysis based on the median absolute deviation approach (MADA). The MADA method for filtering extreme values in a dataset uses the median instead of the mean as a central tendency measure. The median allows for flagging points that do not conform with the sampled data's trends [66]. The MADA index is defined by Equation (36) when a Gaussian distribution assumption is considered for the data without the influence of extreme values [67].  
\[\text{MADA}=1.4826\times\text{Median}[|x_{i}-Median(x)|] \tag{36}\]  
where xi is the value of a given variable at a specified timestep; Median(x) is the median of the variable's sample size. In this study, the criteria for filtering the data for potential outliers was the recommendation by [66]. The median \(\pm\) 2.5 times the MADA index is the cutoff value expected in each sampled dataset.