## 1 The first stage of the study involves preprocessing the data
Figure 2:3  
where \(\hat{y}_{class},\hat{y}_{class}\)--are the predicted and actual labels for classification, \(\hat{y}_{\text{salinity}}\), \(y_{salinity}\)--are the predicted and actual (or pseudo) salinity labels, \(\alpha\)--is a weight constant that determines the contribution of the regression problem to the total error, and \(L\)--is the final loss function used in training the neural network. The following software was used for implementation: Python 3.9.12, TensorFlow 2.12.0 for neural networks, XGBoost 1.6.2 for gradient boosting, Scikit-learn 1.1.3 for clustering and normalization, GDAL 3.4.0 for geodata processing, Matplotlib 3.5.2, and Seaborn 0.11.2 for visualization. Satellite image data were acquired and processed using the Google Earth Engine API. Models were executed on a computation server equipped with an NVIDIA RTX 3090 graphics card (24 GB VRAM) and 128 GB of RAM, sourced from NVIDIA Corporation, Santa Clara, CA, USA, to ensure high computational performance and large data capacity. Satellite data from Sentinel-2 are accessed through the Copernicus Open Access Hub ([[https://www.copernicus.eu/en](https://www.copernicus.eu/en)]([https://www.copernicus.eu/en](https://www.copernicus.eu/en))) (accessed on 20 March 2025). ERA5-Land climate data are accessed from the ECMWF Climate Data Store ([[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu))) (accessed on 20 March 2025). Thus, the proposed method can be reproduced and adapted for other regions, which makes it a universal tool for soil salinity analysis. The model is based on two supplementary sources of information: Sentinel-2 multi-spectral imagery and climate ERA5-Land model data. We utilized both to consider surface reflectance properties and hydrology affecting salinization. The European Space Agency's Sentinel-2 satellite system of the Copernicus program has a multispectral sensor MSI with high-resolution imaging in the wide part of the spectrum (10-60 m). The analysis used 10 m resolution bands: B2 (blue), B3 (green), B4 (red), B8 and B8A (short-wave infrared), and B11 and B12 (mid infrared). These data are necessary to assess vertical salt transport processes, i.e., leaching or build-up as a function of depth and season. In addition to the fundamental spectral and hydrologic properties, the model included derived spectral indices (SII-SI5, NDVI, NDWI, NDSI, BI) that increased the model's sensitivity to vegetation and soil moisture properties. Thus, the set of features covers surface and deep parameters, providing a comprehensive approach to salinity modeling. This expands the analysis capabilities and increases the method's versatility when applied in various natural and climatic conditions. The data were obtained through Google Earth Engine. The following indices were used in this work:  
1. Normalized Differential Salinity Index (NDSI) (13):  
Figure 2:4  
where \(R\)--is the value of the red channel (B4); \(NIR\)--is the near infrared channel (B8). This index is used to assess the level of soil salinity. 2. Brightness Index (BI) (14):  
Figure 2:5  
This index reflects the surface brightness and helps estimate the soil texture. 3. Salinity Index 1 (SI1) (15):  
Figure 2:6  
where B is the value of the blue channel (B2). 4. Salinity Index 2 (SI2) (16): Figure 2:7 (16) where \(G\) is the value of the green channel (B3). 5. Salinity Index 3 (SI3) (17): Figure 2:8 (17) where SWIR1 and SWIR2--are the values of the middle infrared channels (B11 and B12). 6. Salinity Index 4 (SI4) (18): Figure 2:9 (18) 7. Salinity Index 5 (SI5) (19): \[X_{ij}^{\prime}=\log\bigl{(}1+X_{ij}\bigr{)} \tag{1}\]0 (19) 8. Normalized Difference Vegetation Index (NDVI) (20): \[X_{ij}^{\prime}=\log\bigl{(}1+X_{ij}\bigr{)} \tag{1}\]1 (20) 9. Normalized Difference Water Index (NDWI) (21): \[X_{ij}^{\prime}=\log\bigl{(}1+X_{ij}\bigr{)} \tag{1}\]2 (21)