Climate change, uneven distribution of water resources and anthropogenic impact have led to salinization and land degradation in the southern regions of Kazakhstan. Identification of saline lands and their mapping is a laborious process associated with a complex of ground measurements. Data from remote sensing are widely used to solve this problem. In this paper, the problem of assessing the salinity of the lands of the South Kazakhstan region using remote sensing data is considered. The aim of the study is to analyze the applicability of machine learning methods to assess the salinity of agricultural lands in southern Kazakhstan based on remote sensing. The authors present a salinity dataset obtained from field studies and containing more than 200 laboratory measurements of soil salinity. Moreover, the authors describe the results of applying several regression reconstruction algorithms (XGBoost, LightGBM, random forest, Support vector machines, Elastic net, etc.), where synthetic aperture radar (SAR) data from the Sentinel-1 satellite and optical data in the form of spectral salinity indices are used as input data. The obtained results show that, in general, these input data can be used to estimate salinity of the wetted arable land. XGBoost regressor ($R^2$ = 0.282) showed the best results. Supplementing the radar data with the values of salinity spectral index improves the result significantly ($R^2$ = 0.356). For the local datasets, the best result shown by the model is $R^2$ = 0.473 (SAR) and $R^2$ = 0.654 (SAR with spectral indexes), respectively. The study also revealed a number of problems that justify the need for a broader range of ground surveys and consideration of multi-year factors affecting soil salinity. Key results of the article: (i) a set of salinity data for different geographical zones of southern Kazakhstan is presented for the first time; (ii) a method is proposed for determining soil salinity on the basis of synthetic aperture radar supplemented with optical data, and this resulted in the improved prediction of the results for the region under consideration; (iii) a comparison of several types of machine learning models was made and it was found that boosted models give, on average, the best prediction result; (iv) a method for optimizing the number of model input parameters using explainable machine learning is proposed; (v) it is shown that the results obtained in this work are in better agreement with ground-based measurements of electrical conductivity than the results of the previously proposed global model.

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

Learning-enabled control requires training to produce competent policies consistently when success requires every robot to finish. This study evaluates masked entity-attention multi-agent proximal policy optimization (MAPPO) for simulated landmark coverage by five differential-drive robots. Configurations were compared under a fixed reward, optimizer, stopping rule, and 100 held-out scenarios, using trained seeds as statistical units; the actor observes the robot–landmark scene but not the externally computed assignment. Across ten policies per principal configuration, masked entity attention achieved $94.6\pm 4.7\%$ deterministic full-team success, against $9.3\pm 12.4\%$ and $6.8\pm 5.1\%$ for wider (1.21 times its parameters) and ordinary fixed-index controls, every masked-attention seed exceeding every seed of both fixed-index controls. Near-parameter-matched Deep Sets and unmasked-attention controls reached $73.7\pm 14.0\%$ and $72.1\pm 13.1\%$, overlapping it. The same policies retained $91.4$–$96.3\%$ mean success from three to eight robots without retraining, and masked attention remained separated from both fixed-index controls at every rate in a 45-policy learning-rate design. Held-out scenes come from the training distribution; under shift, success fell to $70.9\%$ in an unseen larger workspace, while landmark, sensing, and actuation perturbations kept it above $93.3\%$. Masked entity-attention MAPPO gives the most consistent full-team completion among the tested designs; broader robustness and physical deployment remain unestablished.