## 3 Results and Discussion  
The proposed method demonstrated high efficiency in forecasting and spatial segmentation of soil salinity. In contrast to the basic approach based solely on the K-Means algorithm, the developed system, combining ensemble clustering, probabilistic feature enrichment, and multi-task neural network architecture, showed significant improvement in all key metrics. This confirms the feasibility of integrating machine learning methods when processing complex heterogeneous data typical for remote monitoring tasks. The model is based on two complementary data sources: Sentinel-2 multispectral imagery and climate data from the ERA5-Land model. We utilized both to capture surface reflectance properties and hydrology that affect salinization. The European Space Agency's Sentinel-2 satellite system under the Copernicus initiative carries a multispectral sensor MSI offering imaging with high spatial resolution in a vast portion of the spectrum (10-60 m). The analysis utilized 10 m resolution bands: B2 (blue), B3 (green), B4 (red), B8 and B8A (short-wave infrared), and B11 and B12 (mid infrared). These channels provide information on vegetation cover, humidity, and reflective characteristics of the surface, including hints on mineral composition and potential salinity. We conducted performance testing in both local and cloud computing environments to evaluate the model's suitability for real-world deployment. The tests focused on key metrics such as inference time, processing throughput, memory consumption, and scalability. Table 1 below summarizes the results of two representative platforms: a local Apple M1 Pro system and a cloud-based NVIDIA RTX 3090 server. The model demonstrates high computational efficiency, with inference times under 1.2 s per tile and throughput exceeding 1 megapixel per second in a parallelized setup. The complete processing of a 100 km\({}^{2}\) scene requires less than two minutes in a cloud environment, while the entire Sentinel-2 scene (~120 MPix) can be processed in under 2.5 min using 32-thread queues. The low memory usage (~0.2 GB) and modular structure of the pipeline ensure excellent scalability, making the system well-suited for near real-time applications in large-scale remote sensing and monitoring tasks. These indices play a key role in assessing the condition of the soil, since they directly reflect such indicators as the salinity level and water balance of the upper horizon. The study processed a total of 33,624 rows of data. Of the total dataset containing 33,624 observations, 23,536 rows were used for model training, while the remaining 10,088 rows were allocated for testing and clustering quality assessment. The 23,536:10,088 split (approximately 70:30) is justified through stratified sampling, which preserves the natural class distribution of 34% saline soils, 46% normal soils, and 20% sandy soils. This approach ensures that each class is sufficiently represented in the test set, with at least 1500 observations per class, thereby supporting a confidence interval of \(\pm\)1% for accuracy estimates. Figure 3 shows a heat map of the linear correlation between the features and pseudo-labels of the clusters (Pseudo-Label Index) obtained from clustering. The \(Y\)-axis shows the used features, including spectral indices (NDSI, NDVI, NDWI, SII-SI5, BI) and the hydrological ERA5-Land model variables (VSWL1-VSWL4), whereas the \(X\)-axis shows the cluster indices (0, 1, 2). The color scale represents the correlation level: positive values are shown in red hues, indicating a direct relation, and negative ones are shown in blue, suggesting an inverse relationship. The strongest positive correlation with cluster 0 is observed for all hydrological variables (VSWL1-VSWL4), with VSWL4 (1.00) indicating an association of high soil moisture levels with this cluster type. At the same time, for cluster 1, most features (in particular, SII, SI2, SI5, and NDVI) demonstrate a positive correlation, which may indicate spectral features of salinity.