## 3 Results and Discussion
Cluster 2 is characterized by weak or slightly negative correlations, which may indicate its intermediate or mixed nature. Such visualization allows a better understanding of the contribution of individual features to cluster formation and the interpretation of the physical properties of different soil types identified during clustering. Figure 4 shows the heat map of mutual information between the features and pseudo-label indices of clusters obtained in the clustering process. The vertical axis shows the features, including spectral indices (NDSI, SII-SI5, NDVI, NDWI, BI) and hydrological parameters (VSWL1-VSWL4), and the horizontal axis shows the cluster indices (0, 1, 2). The color scale displays the value of mutual information, where brighter yellow shades correspond to a high level of informativeness of the feature regarding cluster membership. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Metric**} & \multicolumn{1}{c}{**Local Test (Apple M1 Pro \(\bullet\) GPU MPS)**} & \multicolumn{1}{c}{**Cloud Test (NVIDIA RTX**} & \multicolumn{1}{c}{**Notes**} \\ \hline Input tile size & 1024 \(\times\) 1024 px (1.05 MPix) & 1024 \(\times\) 1024 px (1.05 MPix) & Identical input volume \\ Inference time per tile & 1.18 s & 0.97 s & Full pipeline: indices \(\rightarrow\) \\ Throughput & 0.89 MPix/s & 1.08 MPix/s & XGBoost probabilities \(\rightarrow\) DNN \\ Model memory usage & 0.2 GB & 0.2 GB & Inverse of inference time \\ Processing time for a & \(\approx\)118 s & \(\approx\)97 s & Assuming 10 m resolution and \\
100 km\({}^{2}\) scene & \(\approx\)2 min 20 s (with 32-thread & \(\approx\)1 min 50 s (with 32-thread & Near real-time performance \\ (\(\sim\)120 MPix) & queue) & queue) & with horizontal scaling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational performance and scalability of the proposed model in local and cloud environments. Hydroological parameters VSWL1, VSWL2, and VSWL3 for clusters 0 and 1 (0.69) attain maximum mutual information values, confirming their substantial value towards separating the data by the soil moisture level. Among the spectral characteristics, SI4, SI5, and SI1 have the highest mutual information with the labels, especially concerning cluster 0 and cluster 1, and thus are most significant in identifying soil salinity and texture. Mutual information for each feature compared with cluster 2 is lower, which might indicate a less structured or mixed nature of cluster 2. Thus, visualization of mutual information allows us to see the most significant features contributing to the generation of the cluster structure and can be used for additional feature selection and model interpretation. Figure 4: Heat map of mutual information between features and pseudo-labels of clusters. Figure 3: Heat map of linear correlation between features and pseudo-labels of clusters. Figure 5 compares the clustering quality of two models: one simpler with the K-Means algorithm (orange bars) and an ensemble model (blue bars) that combines multiple methods. Two internal evaluation metrics were used: Silhouette Score and Davies-Bouldin Score. The Silhouette Score, which describes how isolated the clusters are, was higher in the ensemble model (0.76754) than in the simple model (0.61703), illustrating increased separation between clusters of objects. Meanwhile, a low Davies-Bouldin Score is better. This score was smaller in the ensemble technique (0.30375 versus 0.46008), illustrating more cluster compactness and less overlapping. These measurements confirm that the ensemble model achieves better quality clustering than the baseline K-Means algorithm. Figure 6 compares external clustering quality metrics for two approaches: a simple model based on the K-Means algorithm (orange bars) and an ensemble model (blue bars) using a combined clustering approach. The Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and the coefficient of determination (R\({}^{2}\) Score) were used as evaluation metrics.