## 3 Results
### Feature Importance  
In the analysis of feature importance presented in the Figure 10, we focus on a collection of Sentinel-2 images captured during three distinct vegetation periods: spring, summer, and autumn. This dataset comprises multiple sets of images, each containing 12 spectral channels, with the exception of channel 10, which corresponds to the Cirrus band, known for its limited utility in vegetation analysis.  
Out of the total of 36 individual images in the entire collection, a significant finding is that 14 of these images account for 50% of the overall information captured. This statistic highlights the effectiveness of using a selective subset of images for analysis, indicating that not all images contribute equally to understanding the vegetation dynamics over the seasons. Notably, the distribution of these key images reveals a dominance of autumn and spring data, with seven images from autumn and five from spring being particularly informative. In contrast, only two images from the summer season were identified as important, suggesting that the spectral characteristics of vegetation may be more pronounced or more easily discernible in the spring and autumn periods.  
Moreover, the analysis indicates a prevalence of infrared channels over visible channels in this dataset. Specifically, there are a total of nine infrared channels compared to just five visible channels. This trend suggests that infrared spectral information is more relevant for capturing the variations in vegetation during these periods. The infrared channels are known for their sensitivity to plant health, moisture content, and biomass, making them critical for understanding vegetation conditions. Therefore, the emphasis on infrared data may provide deeper insights into seasonal changes in vegetation, enhancing the overall analysis and interpretation of the data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **npv** & **ppv** & **tnr** & **tpr** & **ts** \\ \hline
**1** & **0.9575** & 0.8690 & **0.9575** & 0.8690 & 0.7684 \\
**3** & **0.9839** & 0.7879 & **0.9776** & 0.8387 & 0.6842 \\
**4** & **0.9298** & 0.8721 & **0.8785** & 0.9259 & 0.8152 \\
**6** & **0.9044** & 0.6800 & **0.9431** & 0.5484 & 0.4359 \\
**8** & **0.9941** & 0.5000 & **0.9941** & 0.5000 & 0.3333 \\ \hline \hline \end{tabular}
\end{table}
Table 16: RF 2023 ML accuracy metrics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **fdr** & **fnr** & **foRate** & **fpr** & **mcc** \\ \hline
**1** & 0.1310 & 0.1310 & 0.0425 & 0.0425 & 0.8266 \\
**3** & 0.2121 & 0.1613 & 0.0161 & 0.0224 & 0.7937 \\
**4** & 0.1279 & 0.0741 & 0.0702 & 0.1215 & 0.8031 \\
**6** & 0.3200 & 0.4516 & 0.0956 & 0.0569 & 0.5359 \\
**8** & 0.5000 & 0.5000 & 0.0059 & 0.0059 & 0.4941 \\ \hline \hline \end{tabular}
\end{table}
Table 15: RF 2023 ML accuracy metrics (notice “error” metrics foRate and fpr and compare with OME and CME Table 14).  
This exploration of feature importance not only underscores the temporal dynamics of vegetation as observed through Sentinel-2 imagery but also emphasizes the significance of selecting appropriate spectral channels for effective monitoring and assessment of ecological and environmental conditions across different seasons.