## 4 The Importance of Variables
### Farmland  
The spatial and temporal variability of soil salinity in farmland was significantly influenced by various farming and management practices. The \"wash-sow-suppress-return\" management approach in agricultural fields restricts the natural accumulation of salinity compared to natural landscapes [122]. Soil salts undergo multiple transport changes in a very short time, and the regular plowing and fertilization of field soil further obscures salinity information [123]. Inversion methods relying solely on multispectral data and its derivatives prove ineffective. In recent years, to enhance the accuracy of salinity prediction in agricultural soils, some scholars have utilized Sentinel-1 and Sentinel-2 imagery. In the Weibei Plain area located on the southern coast of Laizhou Bay in China, [PERSON] et al. [81] identified soil samples with anomalous spectra and optimized the corrected sample set by constructing a spatial correlation matrix. Their findings indicated that the B3, B8, and B11 bands of Sentinel-2 are crucial for monitoring soil salinity.  
To effectively predict soil salinity in agricultural fields, it is essential to consider not only soil moisture status and vegetation cover but also physiological stress response parameters during the growing season and their effects on soil salinity [124]. Additionally, the texture, geometry, and grain characteristics of the area should be examined. For instance, micro-surface morphological structures can be represented by radar echo intensity. Studies have demonstrated a strong correlation between radar backscatter coefficients and soil salinity [125], although such studies remained limited. To achieve effective monitoring of soil salinity across a broader range of temporal and climatic conditions, [PERSON] et al. [71] fused Sentinel-1 and Sentinel-2 imagery data, using the Hetao oasis farmland in Inner Mongolia as a study area. The results indicated that the accuracy of salinity inversion using VV-polarized Sentinel-1 data in conjunction with Sentinel-2 data surpassed that of single-series data.  
Furthermore, [PERSON] et al. [88] investigated sunflower and maize farmland in the Loop Oasis and highlighted that the chlorophyll content estimation index (NDRE), constructed from the Sentinel-2 red-edge band and near-infrared (NIR) bands, exhibited high importance across all three algorithms: RF, GBRT, and XGBoost. The NDRE index plays an irreplaceable role in estimating the water and salt content of farmland soils. It is less sensitive to changes in soil background but exhibits sensitivity to physiological stress and biomass variations in vegetation, making it advantageous for monitoring early crop water and salt stress conditions [126].  
[PERSON] et al. [26] focused on the soil salinity at a depth of 0-1 m in November within the agricultural fields of Alear in southern Xinjiang. Their research, utilizing the RF algorithm based on IncNodePurity, identified the Global Vegetation Moisture Index (GVMI) as the most significant indicator variable (9.10%). Additionally, [PERSON] et al. [127] confirmed that GVMI serves as an effective indicator of soil salinity, given that the SWIR region is a strong moisture absorption zone while NIR is more sensitive to canopy variations. Thus, GVMI may be a key indicator for estimating soil salinity in arid and semi-arid regions under vegetation cover conditions.  
Farming is the most water-consuming activity in the Tadla and Morocco regions of Africa. Farmers often utilize water extracted from groundwater as supplementary surface water for irrigation. However, the reliance on groundwater for irrigation threatens the sustainability of soil and water resources. The intensification of pumping has led to declining groundwater levels and increasing salinity. If insufficient water is added as part of the leaching process, surface water may become contaminated with poor-quality groundwater, resulting in a buildup of salinity.