One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.

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.