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.

The monetary value assigned to ecosystems and their essential goods and services is known as ecosystem service value (ESV). Fragile ecological environments, susceptible to climate change and human disturbances, require significant ecological protection. This protection is vital not only for stabilizing socio-economic conditions but also for fostering a positive feedback loop within natural systems. Looking ahead, identifying priority conservation areas and regions of particular concern in fragile environments based on ESV changes is a proactive approach. Until now, current studies on ESV in fragile ecological environments have been insufficient, falling short of adequately serving the purpose of ecological protection in such areas. The Hexi region (HXR), situated in the interlaced area of the Qilian Mountains and the Alxa Plateau in northwest China, possesses a fragile ecological environment. Due to the intricate interactions between humans and the environment, it stands out as a crucial area for studying ecosystem services in fragile habitats. Hence, this study aims to systematically analyze the ESV in the HXR over the past 30 years. Here, the values of 11 ecosystem service categories in HXR during 1990–2020 were calculated, clarifying their spatiotemporal difference and dynamic changes based on land use/cover (LULC) products for years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, using an improved equivalent factors method. The findings revealed that the unit standard equivalent factor is 1.51 × 10⁵ CNY·km⁻²·a⁻¹, and the total ESV value has grown from CNY 182.50 to 185.48 billion from 1990 to 2020. Hydrologic regulation, climate regulation, and soil conservation service values are the principal single ESV categories across HXR. Over the past three decades, ESV changes in HXR have been primarily driven by water area, grassland, and wetland, with the sensitivity of ESV to LULC generally increasing (0.15~9.10%). Natural forcing and anthropogenic perturbations have combined to cause changes in LULC in HXR, influencing ESV fluctuations. Future ecosystem protection activities in HXR should prioritize the maintenance of grasslands, water regions, and wetlands to ensure the preservation of ESV. This study clarifies the impacts of LULC change on ESV in fragile natural environments and highlights the significance of focusing on ESV changes for the health promotion of natural-social systems. The findings provide a foundation for constructing a sustainable development model that is in harmony with both people and the environment.

Most of the world’s saline soils are found in arid or semiarid areas, where salinization is becoming serious. Ground laboratory hyperspectral data (analytical spectral devices, ASD) as well as spaceborne hyperspectral data, including Gaofen-5 (GF-5) and Huanjing-1A (HJ-1A), provide convenient salinity monitoring. However, the difference among ASD, GF-5, and HJ-1A spectra in salinity monitoring remains unclear. So, we used ASD, GF-5, and HJ-1A spectra as data sources in Gaotai County of Hexi Corridor, which has been affected by salinization. For a more comprehensive comparison of the three spectra datum, four kinds of band screening methods, including Pearson correlation coefficient (PCC), principal component analysis (PCA), successive projections algorithm (SPA), and random forest (RF) were used to reduce the dimension of hyperspectral data. Particle swarm optimization (PSO) was used to improve the random initialization of weights and thresholds of the back propagation neural network (BPNN) model. The results showed that root mean square error (RMSE) and determination of the coefficients (R²) of models based on ASD and HJ-1A spectra were basically similar. ASD spectra (RMSE = 4 mS·cm⁻¹, R² = 0.82) and HJ-1A (RMSE = 2.98 mS·cm⁻¹, R² = 0.93) performed better than GF-5 spectra (RMSE = 6.45 mS·cm⁻¹, R² = 0.67) in some cases. The good modelling result of HJ-1A and GF-5 data confirmed that spaceborne hyperspectral imagery has great potential in salinity mapping. Then, we used HJ-1A and GF-5 hyperspectral imagery to map soil salinity. The results of GF-5 and HJ-1A showed that extremely and highly saline soil mainly occurred in grassland and the southern part of arable land in Gaotai County. Other lands mainly featured non-saline and slightly saline soil. This can provide a reference for salinity monitoring research.