Soil salinization threatens agricultural productivity, leading to desertification and land degradation. Given the challenges of conducting labor-intensive and expensive field studies and laboratory analyses on a large scale, recent efforts have focused on leveraging remote sensing techniques to study soil salinity. This study assesses the importance of soil salinity indices’ derived from remotely sensed imagery. Indices derived from Landsat 8 (L8) and Sentinel 2 (S2) imagery are used in Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), and Support Vector Machine (SVR) are associated with the electrical (EC) conductivity of 280 soil samples across 24,000 hectares in Northeast Iran. The results indicated that the DT is the best-performing method (RMSE = 12.25, MAE = 2.15, R² = 0.85 using L8 data and RMSE = 10.9, MAE = 2.12, and R² = 0.86 using S2 data). Also, the results showed that Multi-resolution Valley Bottom Flatness (MrVBF), moisture index, Topographic Wetness Index (TWI), and Topographic Position Indicator (TPI) are the most important salinity indices. Subsequently, a time series analysis indicated a reduction in salinity and sodium levels in regions with installed drainage networks, underscoring the effectiveness of the drainage system. These findings can assist decision-making about land use and conservation efforts, particularly in regions with high soil salinity.

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R² = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R² = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation.

Soil organic carbon (SOC) is one of the most important components of soil physical and chemical properties that have an important role in sustainable production in agriculture and preventing soil degradation and erosion. Data mining approaches and spatial modeling besides machine learning techniques to investigate the amount of soil organic ...

This study evaluates the effectiveness of integrating remote sensing data with field observations and machine learning techniques for estimating and mapping available phosphorus (Pav) and exchangeable potassium (Kex) in the Gonbad Kavous region. A total of 394 soil samples collected from the surface layer (0–15 cm depth) were analyzed for Pav and Kex concentrations. Sentinel-2 satellite imagery, together with environmental covariates, was used as predictor variables to model the spatial distribution of soil nutrients. Four machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Trees (BRT), and Generalized Linear Model (GLM) were trained and validated for digital soil nutrient mapping. Among these models, RF achieved the highest predictive performance for both Kex (R² = 0.79, RMSE = 13.39) and Pav (R² = 0.83, RMSE = 2.60), outperforming the other approaches. The SVM model also demonstrated satisfactory performance in capturing spatial variability, while GLM showed comparatively lower accuracy. The results confirm the strong potential of combining Sentinel-2-derived spectral information with machine learning algorithms for high-resolution digital soil mapping. The generated spatial distribution maps of Pav and Kex provide valuable insights for soil fertility assessment and can support precision agriculture, nutrient management planning, and sustainable land management practices in semi-arid agricultural regions. Importantly, this study highlights the robustness of ensemble-based learning methods, particularly Random Forest, for predicting soil nutrient variability using multi-source geospatial data.