Hyperspectral data has attracted considerable attention in recent years due to its high accuracy in monitoring soil salinization. At present, most existing research focuses on the saline soil in a single area without comparative analysis between regions. The regional differences in the hyperspectral characteristics of saline soil are still unclear. Thus, we chose Golmud in the cold–dry Qaidam Basin (QB–G) and Gaotai–Minghua in the relatively warm–dry Hexi Corridor (HC–GM) as the study areas, and used the deep extreme learning machine (DELM) and sine cosine algorithm–Elman (SCA–Elman) to predict soil salinity, and then selected the most suitable algorithm in these two regions. A total of 79 (QB–G) and 86 (HC–GM) soil samples were collected and tested to obtain their electrical conductivity (EC) and corresponding hyperspectral reflectance (R). We utilized the land surface parameters that affect the soil based on Landsat 8 and digital elevation model (DEM) data, selected the variables using the light gradient boosting machine (LightGBM), and built SCA–Elman and DELM from the hyperspectral reflectance data combined with land surface parameters. The results revealed the following: (1) The soil hyperspectral reflectance in QB–G was higher than that in HC–GM. The soils of QB–G are mainly the chloride type and those of HC–GM mainly belong to the sulfate type, having lower reflectance. (2) The accuracies of some of the SCA–Elman and DELM models in QB–G (the highest MAEv, RMSEv, and ${\mathrm{R}}_{\mathrm{v}}^2$ were 0.09, 0.12 and 0.75, respectively) were higher than those in HC–GM (the highest MAEv, RMSEv, and ${\mathrm{R}}_{\mathrm{v}}^2$ were 0.10, 0.14 and 0.73, respectively), which has flatter terrain and less obvious surface changes. The surface parameters in QB–G had higher correlation coefficients with EC due to the regular altitude change and cold–dry climate. (3) Most of the SCA–Elman results (the mean ${\mathrm{R}}_{\mathrm{v}}^2$ in HC-GM and QB-G were 0.62 and 0.60, respectively) in all areas performed better than the DELM results (the mean ${\mathrm{R}}_{\mathrm{v}}^2$ in HC–GM and QB–G were 0.51 and 0.49, respectively). Therefore, SCA–Elman was more suitable for the soil salinity prediction in HC–GM and QB–G. This can provide a reference for soil salinization monitoring and model selection in the future.

Salinity stress severely constrains plant growth and ecosystem functioning in arid and semi-arid regions, and plant growth-promoting rhizobacteria (PGPR) have been increasingly applied to enhance plant salt tolerance. Hoswever, it remains unclear whether different PGPR inoculation strategies confer salt resistance through similar or distinct physiological pathways, particularly in perennial halophytes adapted to saline environments. In this study, a field experiment was conducted to evaluate the effects of single- and multi-strain PGPR inoculation on the growth performance, physiological responses, and stress regulation of Atriplex canescens under saline conditions. Plant biomass allocation, photosynthetic traits, osmotic adjustment substances, antioxidant enzyme activities, and comprehensive stress tolerance indices were systematically assessed. The results showed that PGPR inoculation significantly improved plant growth and stress tolerance; however, the magnitude and underlying mechanisms varied across inoculation strategies. Single-strain inoculation predominantly enhanced root development and antioxidant regulation, whereas multi-strain inoculation tended to promote aboveground growth and photosynthetic performance. In contrast, certain strain combinations did not produce additive benefits, suggesting potential incompatibility among microbial consortia under salt stress. Multivariate analyses further indicated that improvements in stress tolerance were more closely associated with coordinated physiological regulation than with biomass accumulation alone. Overall, our findings demonstrate that PGPR-mediated salt tolerance in A. canescens is strategy-dependent and involves distinct resource allocation and stress-defense pathways. These results highlight the importance of considering inoculation strategies and functional compatibility when applying PGPR to improve plant performance in saline ecosystems.