## 3 Innovation  
### Innovative Research Focusing on Variations in Predicted Target Depths and Unexplored Area  
#### 3.1.1 Soil Depth  
Several studies have applied remote sensing techniques to deep soil salinity inversion, considering it an innovative approach. [PERSON] et al. [35] focused on using remote sensing to detect salinity changes at various depths in their research. In this area, researchers have modeled the quantitative relationships between surface spectra and soil salinity content, exploring advanced techniques for monitoring vertical salinity. However, there is a general consensus that passive remote sensing methods primarily detect soil properties at the surface (0-5 cm). As soil depth increases, the variability of subsurface salinity becomes more complex, making it challenging for surface spectra to accurately reflect deeper salinity conditions [36]. This limitation affects the accuracy of predicting subsurface salinity. Traditional surface soil salinity measurements are insufficient for comprehensively assessing the salinity status of deeper soils, particularly since soil salt moves within the soil profile during tillage and irrigation [37]. To address this challenge, [PERSON] et al. [26] developed a new framework that combines time-series satellite images with ML techniques, enabling the monitoring of total salt content in the soil profile at a depth of up to 1 m. Additionally, [PERSON] et al. [38] raised a critical scientific question in their study that year: how to effectively utilize remote sensing imagery for multi-seasonal and multi-layer salt modeling and trend analysis. This inquiry is of great practical significance for enhancing our understanding of soil salinity dynamics and guiding agricultural production. 1.2 Exploring the Spatial and Temporal Variations in Soil Salinity of Various Crops Across Different Seasons  
The interplay between seasons and crop types significantly affects the accuracy of spatial and temporal inversion of regional soil salinity, making it a complex and critical scientific issue [39]. Vegetation serves as an important indicator of soil salinity and plays a vital role in the inversion process [40]. In regions influenced by monsoon climates, saline vegetation exhibits substantial differentiation [41]. This interaction between saline vegetation, soil, and the surrounding environment contributes to high spatial and temporal heterogeneity in soil salinity [42]. Different types of vegetation display distinct physiological responses to salinity stress during growth, resulting in diverse spectral characteristics [36]. By monitoring sensitive Spectral bands in the red edge region, researchers can assess the growth status and health of vegetation [43]. Currently, there is insufficient research on the effects of crop growth stages and types on remote sensing estimations of soil salinity [39]. [PERSON] et al. [44] focused on representative land types, including bare land, alfalfa fields, and wheat fields within an arid zone oasis. They combined multispectral remote sensing images acquired from unmanned aerial vehicles (UAVs) with field-collected soil salinity data to analyze the relationship between the spectral reflectance in the red edge band of vegetation and soil salinity content, aiming to establish a remote sensing inversion method for soil salinity. Additionally, soil salinity exhibits pronounced seasonal fluctuations due to changes in surface cover [45]. Analyzing multi-temporal remotely sensed imagery can capture variations in surface properties, thus enabling the mapping of soil salinity across different seasons [46]. However, due to the complexities associated with surface cover and regional climate, it remains inconclusive which season of remote sensing data yields the best inversion results [47].