## 1 Introduction
This study assists in enhancing soil remote sensing methods and can be utilized to monitor land degradation in the salimized region. Soil salinization is an environmentally significant problem affecting cultivated fields' productivity, ecosystem functions, and water. The urgency to create soil salinity monitoring and prediction tools is gradually increasing due to climate change and the escalating development of agriculture. Scientists have recently attempted to integrate satellite imaging, machine learning algorithms, and geographic information systems to improve soil salinity prediction and mapping accuracy. One of the new fields of study is using remote sensing and spectral indicators to detect soil salinity. The study [21] analyzed methods for automatically detecting saline lands using Sentinel-2 data and deep learning algorithms. The results showed that combining spectral indices (NDVI, NDSI, BI) with multilayer neural networks allows for the high classification accuracy of saline soils (up to 91.2%). Another study [22] proposed an improved methodology for using hyperspectral data to analyze soil conditions. The data were processed using transformer methods, allowing more accurate modeling of spatiotemporal patterns of salinity changes. In addition, various approaches to integrating climate data and satellite images to predict soil salinity dynamics are being studied. For example, the work [23] proposes a method based on machine learning and ERA5-Land model data that allows predicting changes in soil degradation, taking into account changes in soil temperature and moisture. Ensemble methods, such as gradient boosting and random forest, increased the forecasting accuracy by 15% compared to traditional linear regression models. Another critical area of research is the assessment of agricultural land degradation due to soil salinization. The study [24] presents an approach based on multispectral sensing and machine learning that allows quantifying the degree of soil salinity in arid regions. The authors emphasize that combining unsupervised methods (K-Means clustering, DBSCAN) with supervised methods (XGBoost, neural networks) allows for minimizing classification errors and considering complex nonlinear dependencies in soil characteristics. In addition to remote sensing, approaches based on local measurements and modeling of salinization processes are actively being developed. The study [25] analyzed the possibilities of combining uncrewed aerial vehicle (UAV) data with geostatistical models for mapping soil salinity. The work shows that UAVs, in combination with deep learning methods, can reduce monitoring costs while providing high spatial accuracy. Finally, the study [26] considers an integrated approach combining satellite monitoring, ground-based measurements, and machine learning to predict soil salinity in different regions. The authors emphasize that using ensemble models increases the reliability of forecasts, especially in conditions of limited availability of field data. Thus, modern research demonstrates significant progress in monitoring and forecasting soil salinity. Integrating remote sensing data, climate models, and machine learning algorithms allows the enhancement of the accuracy of saline land classification and the development of predictive models based on spatiotemporal dynamics of soil properties. These are required for sustainable land management, reducing the impacts of soil degradation, and improving agricultural production efficiency.