There is a large amount of saline-alkali land in China. Through the improvement and utilization of saline-alkali land to improve the carbon content in soil, it can not only become a reserve resource of cultivated land or grazing grassland, but also become an important land “carbon sink”. In this study, we performed a comprehensive meta-analysis to identify the impact of improvement and utilization of saline-alkali soil on soil organic carbon (SOC) in China. Our results showed that the soil salt and alkali content in Heilongjiang Province and Jilin Province in China was the highest, with an SOC content between 3.05 and 17.8 g/kg and pH between 8.84 and 9.94. Among the five methods of reclamation, halophyte planting, fertilization, biochar and modifier application, only biochar and modifier application significantly increased the SOC content (p < 0.05). The content of SOC in saline-alkali soil was 2.9–6.3 g/kg before biochar application, and significantly increased to 6.2–13.05 g/kg after biochar application (p < 0.01). The SOC content was 3.05–8.12 g/kg before the application of the modifier, and significantly increased to 3.68–9 g/kg (p < 0.05) after the application of the modifier. After utilization and improvement of saline-alkali land, the total nitrogen, available phosphorus and available potassium also increased significantly (p < 0.05). This study provides a scientific basis for further understanding the improvement and utilization of saline-alkali land in China and its potential for increasing carbon sinks.

To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity–Hue–Saturation and Gram–Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2–L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2–L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models.

Soil total nitrogen (TN) and soil total phosphorus (TP) are key soil quality indicators and provide critical ecological functions in the grasslands. This study analyzed the driving factors of TN/TP in the grasslands of Qinghai Province based on Shapley additive interpretation (SHAP) analysis. Four machine learning methods, namely random forest (RF), XGBoost 3.2.0, support vector machine, and Cubist, were used to establish spatial prediction models for TN/TP. Vegetation factors (Net Primary Production and Normalized Difference Vegetation Index) and precipitation-related factors (Aridity Index and Mean Annual Precipitation) were the most important variables for TN, indicating plant productivity and precipitation are strongly associated with TN accumulation. Elevation and temperature-related factors (Mean Annual Temperature and evapotranspiration) were the most important variables for TP, demonstrating that elevation-mediated temperature was the major factor affecting the TP accumulation. XGBoost and RF were the optimal models for TN and TP, respectively. TN exhibited a decreasing spatial trend from east to west, while the northwestern and southwestern areas showed relatively higher and lower TP, respectively. Total TN and TP stocks were estimated to be 3.57 × 10⁸ t and 0.88 × 10⁸ t, respectively. This study provides data support and suggestions for sustainable soil nutrient management in the grasslands on the Qinghai-Tibet Plateau.