## 1 Introduction
The platforms utilized can be categorized into four types: high spatial resolution sensors (e.g., RapidEye, Worldview, PlanetScope), multispectral sensors, hyperspectral sensors, and synthetic aperture radar (SAR), as well as ground-based visible and near-infrared (VIS-NIR) instruments [13]. The advantages of multispectral remote sensing--such as a wide field of view, real-time information acquisition, and periodic surface coverage--facilitate soil salinity monitoring and mapping, particularly on a large scale [14; 15]. Satellite data from various multispectral sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel series, Landsat series, IKONOS, Environment satellite 1 (HJ-1), Gaofen-5 (GF-5), and Gaofen-1 (GF-1), have been employed to extract global salinity information at different spatial resolutions. Previous studies have extensively utilized multispectral remote sensing, particularly Landsat and Sentinel-2 satellite data, for soil salinity assessment [4; 11]. However, the wide spectral channels and limited bands of multispectral satellites lead to poor spectral detail and a lack of bands sensitive to soil salinization. Hyperspectral remote sensing satellites offer a promising solution to these challenges. Hyperspectral remote sensing data, particularly from Hyperion and HJ-1, have been initially applied to quantify soil salivization [16; 17]. However, EO-1 satellites are currently out of service, and HJ-1 data lack short-wave infrared bands (SWIR) and finer spatial resolution [17]. Although these hyperspectral satellites can effectively monitor soil salinity, the accuracy of models constructed using hyperspectral data was comparable only to that of Landsat data. The limited estimation accuracy can be attributed to an insufficient signal-to-noise ratio. Consequently, high-precision soil salinity estimation necessitates finer spatial and spectral resolutions, along with higher signal-to-noise ratios. The Gaofen-5 satellite, equipped with the Advanced Hyperspectral Imager (AHS), demonstrates outstanding performance as an operational hyperspectral satellite [18]. With the continuous advancement of airborne and sensor technologies, a complementary global observation network characterized by multi-level, multi-angle, all-weather, and multi-resolution capabilities was gradually taking shape [19]. In recent years, innovative data sources such as digital photographs [20], airborne ground-penetrating radar (GPR) [21], and GNSS-R [22] have begun to be utilized for monitoring primary and secondary soil salinization. Researchers have successfully constructed virtual satellite constellations by integrating multi-source sensors to support urban and ecological remote sensing studies [23]. Soil surveys have transitioned from traditional methods to digital soil mapping (DSM) approaches, which offer more efficient and economically viable tools and techniques for modeling the extent and severity of soil salinity across extensive and data-scarce regions [24; 25]. In DSM, machine learning (ML) algorithms were utilized to quantify the relationships between soil properties and environmental covariates and have been previously employed to map and analyze the spatial and temporal variability of soil salinity [26; 27]. Environmental covariates in DSM are primarily derived from digital elevation models, remotely sensed (RS) data and other detailed geospatial data sources. Numerous ML models have been implemented in DSM to predict soil salinity [5; 28]. The most commonly used ML models include Random Forest [29], Support Vector Regression (SVR) [30], Artificial Neural Network (ANN) [31], Genetic Programming [32], stochastic gradient tree boosting (SGB) [33], LightGBM [34], and XGBoost [28]. Additionally, deep learning algorithms, including DNN and CNN, were increasingly being applied in salinization studies [31].