## 4 The Importance of Variables  
### Oasis  
Topographic variables determine the direction of runoff water movement, which subsequently affects the accumulation pattern and distribution of salts in oasis soils. These topographic factors play a crucial role in the distribution of soil salinity in oases. The study by [PERSON] et al. [118] confirmed a significant correlation between digital elevation models (DEM) and their derivatives with soil salinity, highlighting their potential for soil salinity monitoring. Based on research conducted in a typical oasis in the Xinjiang region of China [34], DEM was found to significantly influence the degree of soil salinity in both study areas. Notably, soil salinization was more sensitive to human activities in the Manas River Basin, located on the northern border, while in the Weigang-Kuche River delta oasis on the southern border, it was more affected by natural factors. In the border zone between farmland and desert in the Kuche River oasis, variables identified through [PERSON]'s algorithm indicated that PlanetScope's B8 and B5 bands were the most critical, with the B8 band being more significant than the B5 band. This finding emphasizes the close relationship between the near-infrared band (B8) and soil conductivity [58]. [PERSON] et al. [9] developed an electrical conductivity (EC) inversion model in the Aksu River basin in southern Xinjiang using topographic attributes and Landsat 8 OLI indices, achieving an R\({}^{2}\) value of 0.92, which demonstrates the spectral features and modeling value in the field of remote sensing applications. In a study conducted in Pingluo County, located in the northern part of the Yinchuan Plain in the Ningxia Hui Autonomous Region, the variable importance ranking based on the Gradient Boosting Machine (GBM) revealed that elevation (DEM), slope, slope direction, plane curvature, profile curvature, and the  
Figure 4: Impact of metadata on prediction accuracy: (**a**) Relationship between calibration model and validation model; (**b**) Effect of number of observation samples on accuracy of calibration model; (**c**) Effect of sample observation depth on accuracy of calibration model; (**d**) Effect of number of variable types on accuracy of calibration model; (**e**) Relationship between number of variables and calibration model; (**f**) Effect of measurement time within year on calibration model; (**g**) Relationship between mean value of measurement values and accuracy of calibration model; (**h**) Relationship between maximum value of measurement values and accuracy of calibration model. Orange dots indicate significant relationships (\(p<0.05\)), black boxes indicate no significant relationships. topographic humidity index all contributed to soil conductivity modeling [90]. In a study mapping soil salinity under different soil types in the Songnen Plain, China, [PERSON] et al. [69] employed the Stacking Machine algorithm and assessed the impact of fused image data on soil salinity prediction accuracy. The importance ranking in this study highlighted the critical roles of the short-wave infrared water stress index (SIWSI) and salinity index in predicting soil salinity. [PERSON] concurs with the findings of [PERSON] et al. [118] and [PERSON] et al. [119]. All of their studies indicated that topographic covariates (e.g., MrVBF and humidity index) and remotely sensed derived covariates (e.g., salinity index and NDVI) are significant predictors for forecasting oases in central Iran. Similarly, [PERSON] et al. [120] emphasized the importance of topographic covariates (e.g., humidity index and slope) and remotely sensed derived covariates (e.g., NDVI and SAVI) for salinity mapping. It is noteworthy that other studies on oasis salinity mapping have exclusively relied on remotely sensed covariates. For instance, [PERSON] et al. [121] demonstrated the effectiveness of six remote sensing-based salinity indices in predicting soil salinity in Ethiopia. Moreover, the findings of [PERSON] et al.