In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation.

To address the severe challenge of soil salinization, effective soil amelioration methods are urgently needed; however, current research on the microbial mechanisms of the combined application of multiple amendments is insufficient. Therefore, this study aims to investigate the impacts of biochar combined with humic acid (HA) on the physicochemical properties and microbial community structure of saline–alkali soils by a field experiment. The results showed that the co-application treatments significantly improved soil physicochemical properties and increased bacterial community richness; specific effects depended on the biochar feedstock. Notably, the H-MBC treatment was the most effective in reducing soil electrical conductivity (EC) by 44.1%, while the H-SBC treatment most significantly increased soil water content by 80.3%. Stochastic processes influenced the assembly of microbial communities, particularly the co-application group, forming a more complex and stable microbial network. Furthermore, Spearman correlation and random forest analyses revealed EC, nitrate nitrogen, and available phosphorus as the primary variables affecting microbial communities. These findings support the potential of the combined application of biochar and HA for saline–alkali soil amelioration, as this strategy mitigates salt stress and increases nutrient availability, thereby reshaping microbial communities toward states conducive to ecological restoration.

Soil organic carbon (SOC) plays a critical role in the terrestrial carbon cycle, yet its spatial patterns and drivers in arid regions remain poorly understood. This study aims to clarify SOC distribution mechanisms in the Akesai region, where limited water–heat conditions and land use create high environmental heterogeneity. Four machine learning models were applied to predict SOC content and produce high-resolution spatial maps, and SHAP analysis was used to quantify the contributions of key environmental variables. The Gradient Boosting model had the best performance (R² = 0.675; RMSE = 1.304 g kg⁻¹), followed by XGBoost, LightGBM, and Random Forest. The results indicated that the main factors controlling SOC variation were NDVI, DEM, sand, clay, mean temperature, and ERVI. Furthermore, NDVI and clay parameters were positively associated with promoted SOC accumulation, while sand showed a negative effect. Spatially, higher SOC values were found in mountainous zones and vegetated valleys, while low SOC values were observed in flat, arid plains. These findings demonstrate that incorporating vegetation-type indicators substantially improves large-scale SOC estimation and enhances our understanding of SOC spatial dynamics and the driving mechanisms in arid environments. This provides a scientific basis for carbon-stock assessment and sustainable land management.

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions.