Soil salinity is a crucial parameter affecting soil health. Excessive surface salt accumulation degrades soil structure, inhibits vegetation growth, and diminishes plant diversity. Such increases in salinity can accelerate desertification, leading to soil resource loss, hampering agricultural progress, and compromising ecological security. However, the vastness of arid regions and data acquisition challenges often hinder efficient SSC monitoring and modeling. In this study, we leveraged remote sensing data coupled with machine learning techniques to investigate the spatio-temporal dynamics of SSC in a representative desert natural forest area, the Alxa National Public Welfare Forest. Utilizing the geodetector model, we also delved into the factors influencing SSC. Our results underscored the effectiveness of the Convolutional Neural Networks (CNN) model in predicting SSC, achieving an accuracy of 0.745. Based on this model, we mapped the spatial distribution of SSC, revealing hydrothermal conditions as pivotal determinants of salt accumulation. From 2016 to 2021, soils impacted by salinity in the research area exhibited a rising trend, attributed to the prevailing dry climate and low precipitation. Such intensified salinity accumulation poses threats to the healthy growth of protective forest vegetation. This study can provide a theoretical reference for salinization management and ecological protection in desert natural forest areas.

Digital soil mapping (DSM) is an effective approach for assessing soil organic carbon (SOC) at regional scales. With the increasing availability of geospatial datasets, a growing number of environmental covariates have been incorporated into SOC mapping. However, the influence of covariate selection on model predictive performance remains poorly understood and has not been systematically quantified. In this study, 2865 observations of SOC stocks from 0–100 cm soil profiles and 147 environmental covariates were compiled across northwestern China. The extreme gradient boosting (XGBoost) algorithm was employed to evaluate the effects of covariate quantity on model performance and to generate a 30 m resolution SOC stock map. The results show that model performance increased rapidly with the inclusion of additional covariates before gradually reaching a plateau. The model achieved 90% of the maximum Lin’s Concordance Correlation Coefficient (LCCC) using 13 covariates and 90% of the maximum coefficient of determination (R²) with 26 covariates. Further increases in covariate numbers continued to improve error metrics, with root mean square error (RMSE) and mean absolute error (MAE) reaching stable levels. Considering multiple performance metrics, model accuracy reached a high and stable level when 26–47 covariates were included. In this study, 47 environmental covariates were selected to construct the SOC stock prediction model. Our SOC map outperformed existing SOC products in capturing the spatial heterogeneity of SOC stocks across northwestern China’s mountain–oasis–desert ecosystems, particularly over complex mountainous terrain. Furthermore, our model further enabled the identification of the regional-scale drivers of temperature and precipitation on the spatial distribution of SOC stocks, as well as the quantification of the local-scale effects of topographic factors, including elevation and slope aspect, on SOC stocks. This study establishes a methodological framework for high-resolution regional SOC stocks mapping, highlighting the importance of optimizing environmental covariate selection to balance predictive accuracy and model complexity. The findings provide practical guidance for efficient and reliable DSM and generate valuable high-resolution SOC stocks data for further carbon cycle studies in arid regions.

Plant diversity is often expected to enhance soil organic carbon (SOC) storage through greater and more heterogeneous plant inputs, but its relationships with functionally distinct SOC fractions in natural forests remain uncertain. This study compiled published SOC fraction data from 341 surface soil observations in natural forests across China and spatially matched these records with gridded plant alpha diversity, forest age, climate, topographic and soil properties datasets to evaluate biotic and abiotic associations with SOC, particulate organic carbon (POC), mineral-associated organic carbon (MAOC) and MAOC/SOC. Linear regression, multiple regression, piecewise structural equation modelling and stratified analyses were used to evaluate whether plant diversity was associated with the absolute accumulation and relative stabilization of SOC fractions. Plant alpha diversity was negatively associated with ln[SOC], ln[POC] and ln[MAOC] at the national scale, whereas its bivariate relationship with MAOC/SOC was weak. After accounting for forest age and environmental covariates, plant alpha diversity remained negatively related to the absolute contents of SOC fractions while showing a positive association with MAOC/SOC. Forest age was positively associated with ln[SOC], ln[POC] and ln[MAOC], and POC was more strongly related to plant diversity and forest age than MAOC. In contrast, MAOC and MAOC/SOC were more strongly associated with mineral protection potential, soil pH and precipitation background. Structural equation models indicated that mineral protection potential and mean annual precipitation were associated with greater MAOC accumulation and SOC allocation to the mineral-associated fraction, whereas temperature and topography were linked to MAOC partly through indirect associations with soil physicochemical conditions. Stratified analyses showed that plant diversity associations varied among forest types and climatic backgrounds. Additional interaction models showed that mineral protection potential significantly moderated the associations between plant alpha diversity and ln[SOC], ln[POC] and ln[MAOC], with negative diversity associations weakening under higher mineral protection potential. These findings indicate that plant diversity associations with SOC fractions in natural forests cannot be interpreted as universally positive input relationships. Instead, their direction and strength depend on hydroclimatic context and soil mineral protection, especially for the absolute accumulation of SOC fractions.