## 3 Results and Discussion
Thus, the proposed method combines the strengths of both unsupervised techniques that highlight the structure of the data and supervised methods that refine the boundaries of classes, providing more accurate, robust, and interpretable predictions. Figure 8 presents the data projected onto the first two principal components (PC1 and PC2), obtained through Principal Component Analysis (PCA). PCA was applied exclusively for visualization purposes, and the first two components explain 97.8% of the total variance, which justifies their use in the diagram. The points are colored according to the predicted labels of the simple model, with each color representing a different class. The visualization reveals that the data are generally grouped into distinct segments; however, some overlap remains between clusters, and not all boundaries are clearly defined. This suggests that while the model can achieve broad separation, it may produce marginal classification errors in cases where spectral feature distributions are insufficiently distinct or overlapping. Figure 9 visualizes the clustering results. It also uses principal components for a two-dimensional data projection. Still, the final labels were obtained by combining several clustering methods (K-Means, agglomerative clustering, and DBSCAN) and feature enrichment via XGBoost. The graph shows a more precise separation of objects into groups: clusters are located separately, with noticeable gaps between them. This suggests that the proposed approach was able to better account for multidimensional dependencies in the data, which led to a more accurate and robust segmentation. Figure 8: Visualization of simple model predictions (classification). The ensemble model shows a more convincing result in clustering quality and separation clarity. It not only forms well-distinguished clusters but also has a smaller number of borderline objects that are difficult to classify. This is also confirmed by the numerical metrics, which indicate higher Silhouette values and a lower Davies-Bouldin value for the proposed approach. In Figure 10, the Predicted Class is plotted horizontally, and the True Class is plotted vertically. The numbers inside the cells show how many objects of each actual class (0, 1, or 2) the model assigned to a particular predicted class. It is clear that almost all objects are concentrated on the diagonal: 3796 from class 0 were correctly predicted as 0, 4580 from class 1 as 1, and 1711 from class 2 as 2. The only error is one object of actual class 0, which was erroneously assigned to class 2 (cell in the first row, third column). Thus, the presented results demonstrate that the proposed model effectively copes with spatial segmentation and quantitative soil salinity assessment, surpassing the baseline model based on the K-Means algorithm in all key metrics. High values of Silhouette Score and ARI, a decrease in Davies-Bouldin Score, and minimal values of MAE and RMSE errors confirm the structural clarity of the clusters and the high accuracy of numerical predictions. PCA data visualization showed that the ensemble model forms more isolated  
Figure 10: Confusion Matrix of the classification model performance. Figure 9: Hybrid clustering (PCA). and interpretable groups, consistent with the numerical metrics. The minimum number of classification errors (only one out of more than 10 thousand records) indicates a high reliability of the identified classes. All this confirms that a comprehensive combination of unsupervised methods, probabilistic enrichment, and neural network multi-task learning provides more accurate, robust, and practical results than traditional approaches. To assess the reliability of the ERA5-Land soil moisture data used in this study, we independently validated the volumetric water content in the topsoil layer (swv1l, 0-7 cm) across the three selected regions. For this purpose, daily soil moisture data from the SMAP L3 passive radar product (spatial resolution 9 km) for 2020-2023 were used as reference.