## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Algorithm** & \multicolumn{3}{c}{**LAI**} & \multicolumn{3}{c}{**FAPAR**} \\ \cline{3-7}  & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{2015} & RF & **0.560** & **0.757** & 0.621 & **0.509** & **0.054** & **0.043** \\  & XGB & 0.528 & 0.778 & **0.614** & 0.451 & 0.057 & 0.045 \\  & SVM & 0.494 & 0.800 & 0.656 & 0.454 & 0.057 & 0.046 \\  & Ensemble & 0.477 & 0.801 & 0.641 & 0.446 & 0.057 & 0.045 \\ \hline \multirow{4}{*}{2016} & RF & **0.389** & **0.791** & **0.638** & **0.441** & **0.048** & **0.039** \\  & XGB & 0.322 & 0.867 & 0.697 & 0.360 & 0.053 & 0.043 \\  & SVM & 0.295 & 0.849 & 0.691 & 0.362 & 0.051 & 0.041 \\  & Ensemble & 0.283 & 0.841 & 0.680 & 0.313 & 0.052 & 0.041 \\ \hline \multirow{4}{*}{2017} & RF & **0.484** & **0.809** & **0.648** & **0.416** & **0.066** & 0.047 \\  & XGB & 0.367 & 0.898 & 0.696 & 0.306 & 0.086 & 0.055 \\  & SVM & 0.436 & 0.829 & 0.649 & 0.408 & **0.066** & **0.046** \\  & Ensemble & 0.389 & 0.854 & 0.662 & 0.298 & 0.071 & **0.046** \\ \hline \multirow{4}{*}{2018} & RF & **0.386** & **0.771** & **0.628** & **0.375** & **0.048** & **0.038** \\  & XGB & 0.321 & 0.817 & 0.647 & 0.328 & 0.049 & 0.039 \\  & SVM & 0.323 & 0.803 & 0.644 & 0.315 & 0.049 & 0.039 \\  & Ensemble & 0.283 & 0.805 & 0.646 & 0.293 & 0.049 & 0.039 \\ \hline \multirow{4}{*}{2019} & RF & **0.459** & **0.855** & **0.674** & **0.393** & **0.064** & **0.042** \\  & XGB & 0.397 & 0.916 & 0.702 & 0.364 & 0.067 & 0.044 \\  & SVM & 0.357 & 0.938 & 0.737 & 0.303 & 0.068 & 0.045 \\  & Ensemble & 0.398 & 0.875 & 0.679 & 0.251 & 0.074 & 0.045 \\ \hline \multirow{4}{*}{2020} & RF & **0.502** & **0.635** & 0.446 & **0.590** & **0.041** & 0.030 \\  & XGB & 0.453 & 0.651 & 0.442 & 0.585 & **0.041** & **0.029** \\ \cline{1-1}  & SVM & 0.453 & 0.680 & **0.431** & 0.558 & 0.043 & **0.029** \\ \cline{1-1}  & Ensemble & 0.429 & 0.667 & 0.461 & 0.519 & 0.043 & 0.031 \\ \hline \multirow{4}{*}{2021} & RF & **0.250** & **0.662** & 0.511 & **0.358** & **0.046** & **0.037** \\ \cline{1-1}  & XGB & 0.176 & 0.720 & 0.535 & 0.293 & 0.049 & 0.038 \\ \cline{1-1}  & SVM & 0.207 & 0.685 & **0.495** & 0.319 & 0.047 & **0.037** \\ \cline{1-1}  & Ensemble & 0.166 & 0.701 & 0.530 & 0.255 & 0.048 & 0.038 \\ \hline \hline \end{tabular}  
* Bolded values indicate the most accurate model per study year.  
\end{table} 
Table 2: Prediction accuracy results of peak LAI and FAPAR data per evaluated machine learning method according to 10-fold cross-validation. From 2015 to 2021, various algorithms were employed to optimize hyperparameters for the LAI and FAPAR (Table A1). For RF, the optimal mtry values oscillated between 2 and 94, with a notable switch to mtry = 94 in 2018 and 2020. XGB consistently utilized nrounds of 50, with fluctuations in lambda, alpha, and eta. In 2016 and 2017, for instance, lambda was set at 0.1, while alpha varied from 0.1 to 1 \(\times\) 10\({}^{-4}\). SVM parameters exhibited slight adjustments, particularly in sigma, which ranged from 0.004 to 0.008 across the years, while the constant C was maintained at 1. The results of the K-means unsupervised classification are presented in Table 4, showing the class centers for the LAI and FAPAR from 2015 to 2021. The observed differences among classes, despite the proximity of their LAI and FAPAR mean values, can be attributed to variations in temporal dynamics, correlation patterns, environmental factors, and statistical distributions of these indices. Temporal trends may reveal distinct intra-annual variability, with some classes exhibiting consistent seasonal peaks, while others display irregular fluctuations. The relationship between the LAI and FAPAR also differs across classes, as a higher LAI does not always translate into higher FAPAR due to variations in canopy structure or photosynthetic efficiency. The mean LAI and FAPAR values for each class are also provided, along with the corresponding ranks according to FAO standards.