## 1 Introduction  
Soil salinization is one of the key environmental problems directly affecting crops, ecosystem functioning, and regions' water regimes [1, 2, 3]. In climate change conditions and agricultural land use intensification, the extent of soil degradation [4, 5, 6] increases incredibly rapidly. Hence, developing effective monitoring and prediction tools for salinity is imperative. Satellite data, in particular multi-band images from Sentinel-2 platforms [7], combined with climate models such as ERA5-Land, provide extensive information on the state of the soil cover. Still, their processing requires modern data analysis methods [8]. Currently, the scientific literature presents various approaches to assessing soil salinity, including spectral indices such as NDVI, NDWI, and NDSI [9; 10], as well as machine learning using classification [11] and regression [12; 13] methods. However, classical supervised learning methods face significant challenges due to the lack of reliable ground-truth labels and the heterogeneity of spectral feature distributions. Additionally, several limitations of the proposed approach must be acknowledged. Due to atmospheric scattering effects, the performance of spectral indices may deteriorate under dense vegetation or in the presence of high aerosol content. Moreover, the spatial resolution of ERA5-Land climate covariates remains coarse for detailed local analysis, and comprehensive in situ soil salinity and conductivity validation is still in progress. These factors may limit the model's generalizability beyond arid and semi-arid environments. In this regard, in recent years there has been an increased interest in hybrid methods combining unsupervised algorithms, ensemble techniques, and deep neural networks to improve forecasting accuracy. One of the pressing challenges in this area is the need to consider the spectral features of different soil types, including saline, regular, and sandy soils, since the spectral characteristics of sandy soils can overlap with signals from saline areas. Moreover, an important task is developing algorithms that can adaptively segment data in high spatial and spectral variability conditions. In this paper, we propose a hybrid model combining unsupervised algorithms (K-Means [14; 15], agglomerative clustering, DBSCAN [16; 17]) with supervised methods (XGBoost [18; 19; 20], multi-task neural network). The initial stage involves clustering the data using the K-Means method to form pseudo-labels, which are then used to train an XGBoost model that calculates the probabilities of objects belonging to specific classes. These pseudo-labels represent a coarse three-class partitioning of the input dataset, where each pixel is assigned to one of the clusters based on spectral similarity. Although these clusters do not correspond to expert-annotated labels, they serve as surrogate target variables that guide the learning process of the subsequent supervised model. This semi-supervised strategy allows leveraging large volumes of unlabeled remote sensing data while preserving meaningful structural groupings in the feature space. Next, an ensemble approach is applied with the voting of several clustering algorithms, after which the final labels are used to train a multi-task neural network that simultaneously solves the problems of classification and regression of soil salinity. The study hypothesizes that the proposed approach will provide more accurate soil segmentation by salinity compared to traditional clustering and classification methods. The experiments will evaluate key metrics of the model quality, including the Silhouette Score, Davies-Bouldin Score, Calinski-Harabasz Score, and the impact of the hybrid ensemble on the quality of the final predictive model. The study's main findings are that applying the hybrid approach improves the accuracy of salt-affected soil segmentation, minimizes the influence of boundary effects between different soil cover types, and adapts the model to the spatial heterogeneity of the data.