## 1 Introduction  
Soil salinization creates a significant threat to agricultural production and reduces the limited soil resources. Typically, it occurs in arid areas where the groundwater level is shallow while the salt content is elevated to the ground by the high evaporation, or it occurs in coastal areas affected by seawater erosion [1]. Climate, landscape, underground water, and many other factors may lead to soil salinization [2]. The Yellow River Delta is one of China's most significant agricultural areas, facing severe soil salinization challenges, with many farmlands suffer from high level soil salinity [3], as a result of the two main factors of seawater erosion [4] and elevated salt content [5]. So, the monitoring and analyzing of soil salinity levels is crucial for sustainable agriculture. However, large-scale field monitoring is challenging and costly, making satellite imagery an economically viable alternative [6]. Since the 1960s, remote sensing technology has been widely employed to predict soil salinity due to its high spatiotemporal resolution, rapid data acquisition, and low cost [7]. Recent studies have quantitatively explored soil salinization by using various spectral indicators derived from remote sensing images [8]. Indicators such as the ratio vegetation index (RVI), modified soil adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), normalized salinity index (NDSI), and salinity index (SI) have been employed to enhance prediction accuracy [9]. For example, [PERSON] et al. [10] combined various indicators to predict soil salinity in the Kenli county of the Yellow River Delta, finding that the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), salinity index (SI3), and salinity index (SI5) can effectively enhance prediction accuracy. [PERSON] et al. [11] used the green difference vegetation index (GDVI), enhanced vegetation index (ENDVI), salinity index (SI-T), normalized difference water index (NDWI), and land surface temperature index (LST) to construct three models--BP neural network (BPNN), support vector machine (SVM), and random forest (RF)--for the inversion of soil salinity at different depths. [PERSON] et al. [12] analyzed soil salinization in the Yellow River Delta from 2000 to 2020 using five indicators, as follows: modified soil adjusted vegetation index (MSAVI), salinity index (SI), kernel normalized difference vegetation index (KNDVI), three-band gradient difference vegetation index (TGDVI), and NDVI. In addition, scholars have also employed mathematically transformed methods to construct variables and reduce noise in the original data. For instance, [PERSON] et al. [13] employed mathematical transformations such as \((\mathbf{1}/\mathbf{R})\), \(lg\mathbf{R}\), \(\mathbf{R^{0.5}}\), \(\mathbf{1}/lg\mathbf{R}\), and first-order derivative \(\mathbf{R^{{}^{\prime}}}\) while using light gradient boosting machine (LightGBM) for variable selection. They combined surface parameters to construct the sine-cosine algorithm (SCA) and deep extreme learning machine (DELM) models for soil salinity inversion. [PERSON] et al. [14] constructed a partial least squares regression (PLSR) model to visualize salinization areas using first-order derivative \(\mathbf{R^{{}^{\prime}}}\), second-order derivative \(\mathbf{R^{{}^{\prime\prime}}}\), \(\mathbf{R^{0.5}}\), \((\mathbf{1}/\mathbf{R})\), and first-order derivative \((\mathbf{1}/\mathbf{R})^{{}^{\prime}}\). [PERSON] et al. [15] used original band \(R\), \(lg(\mathbf{1}/\mathbf{R})\), and continuous removal \(R\_cr\) to build PLSR and SVM models, finding that the SVM performed better. These studies indicate that spectral indicators and mathematically transformed variables can be employed to construct soil salinity inversion models, thus optimizing model performance. From the above text, it is evident that scholars have conducted extensive research on monitoring and predicting soil salinization using remote sensing technology.