## 2 Materials and Methods
### Machine Learning Analyses
#### 2.3.1 Recursive Feature Elimination  
Feature selection is often utilized in machine learning frameworks to identify and preserve the most important features, reducing overfitting, improving model performance, and reducing multicollinearity issues [53]. In our study, we used recursive feature elimination (RFE) with the \"caret\" package (v. 6.0.94) [54]. RFE is a widely used feature selection algorithm that helps reduce or eliminate dependencies and collinearity among predictors. It follows a backward selection procedure: starting with a model built using all the features, the algorithm calculates their importance (based on RMSE) and removes the least important predictors at each iteration. This process continues until the optimal number of predictors--those providing the lowest RMSE--is identified [55]. To implement the RFE, we adopted a bootstrap approach combined with Bayesian Ridge Regression. Bootstrap is a resampling algorithm that builds new datasets by sampling with replacement from the original data. In each bootstrap iteration, a Bayesian Ridge Regression model (\"method = bridge\") is trained, combining a prior distribution (initial assumption about the coefficients) with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bands** & **Central Wavelength** & **Band Width** & **Spatial Resolution** \\  & **(nm)** & **(nm)** & **(m)** \\ \hline b2—Blue & 492.7 & 65 & 10 \\ b3—Green & 559.8 & 35 & 10 \\ b4—Red & 664.6 & 30 & 10 \\ b5—Vegetation red edge & 704.1 & 14 & 20 \\ b6—Vegetation red edge & 740.5 & 14 & 20 \\ b7—Vegetation red edge & 782.8 & 19 & 20 \\ b8—NIR & 832.8 & 105 & 10 \\ b8a—Vegetation red edge & 864.7 & 21 & 20 \\ b11—SWIR & 1613.7 & 90 & 20 \\ b12—SWIR & 2202.4 & 174 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Name, spectral, and spatial characteristics of the Sentinel-2A bands used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Indices** & **Formula** & **Ref.** \\ \hline Enhanced Vegetation Index (EVI) & \(2.5\,^{\star}\) (\(\text{b8}-\text{b4}\))/((\(\text{b8}+6.0\,^{\star}\text{b4}-7.5\,^{\star}\text{b2})+1.0\)) & [46] \\ Modified Chlorophyll Algorithm in Reflectance Index (MCARI) & (\((\text{b5}-\text{b4})-0.2\,^{\star}\) (\(\text{b5}-\text{b3}\))) * (\(\text{b5}\)/b4) & [46] \\ Normalized Difference Red-Edge (NDRE) & (\(\text{b8}-\text{b5}\))/(\(\text{b8}+\text{b5}\)) & [47] \\ Normalized Difference Vegetation Index (NDVI) & (\(\text{b8}-\text{b4}\))/(\(\text{b8}+\text{b4}\)) & [48] \\ Normalized Difference Water Index (NDWI) & (\(\text{b8}-\text{b12}\))/(\(\text{b8}+\text{b12}\)) & [48] \\ Optimized Soil Adjusted Vegetation Index (OSAVI) & (\(1.0+0.16\,^{\star}\) (\(\text{b8}-\text{b4}\))/(\(\text{b8}+\text{b4}+0.16\)) & [49] \\ Transformed Chlorophyll Algorithm Ratio (TCARI) & \(3.0\,^{\star}\) (\((\text{b5}-\text{b4})-0.2\,^{\star}\) (\(\text{b5}-\text{b3}\))) * (\(\text{b5}\)/b4) & [49] \\ Renormalized Difference Vegetation Index (RDVI) & (\(\text{b8}-\text{b4}\))/sqrt (\(\text{b8}+\text{b4}\)) & [50] \\ Simple Ratio NIR/G Green Ratio Vegetation Index (GRVI) & b8/b3 & [51] \\ Simple Ratio Water Index (SRWI) & b4/b12 & [52] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vegetation indices calculated in this study, with their formula and reference.  
the observed data to obtain a posterior distribution [56]. Based on this posterior distribution (which quantifies uncertainty in the coefficients), RFE iteratively removes the least important predictors, converging on the subset that optimizes the model performance.