## 6 Uncertainty and Challenge
### Challenges in Data and Methods
[PERSON] et al. [146] found that PDS yielded superior SOC prediction accuracy compared to both DS and EPO. [PERSON] et al. [149] compared DS and EPO on 0-20 cm soil samples collected from farmland in New Zealand, demonstrating that both methods effectively corrected moisture effects, with EPO outperforming DS. We believe that this principle can be employed to mitigate the influence of soil moisture on soil salinity. The modeling of soil salinity typically focuses on the surface layer, ranging from 0 to 20 cm, with some studies extending the analysis to 60 cm; however, very few investigations address depths of 1 m. Vegetation growth is influenced not only by the fixed effects of soil salinity at specific depths but also by the varying impact of soil conditions on the root system over time. Given that root length differs significantly among crop types, relying on soil salinity data at a fixed depth to correlate vegetation characteristics at a particular moment may hinder optimal modeling. Thus, accurately quantifying the effects of soil changes at various depths over time on root growth presents a significant challenge in current research. Currently, the generalizability of soil salinity modeling across time scales has not been sufficiently explored. Existing studies typically focus on constructing models for specific periods within the growing season. Future remote sensing estimation models must comprehensively account for the interactions between soil physicochemical properties and environmental systems, elucidating how these factors influence the transport processes of soil salinity over time. The issue of mixed pixel spectra in remote sensing estimations should not be overlooked. In areas of natural vegetation growth, the spectra of bare soil inevitably blend with those of vegetation, while various features (e.g., bare soil, mulch, weeds) within agricultural backgrounds can distort the spectral profiles of crops. Moreover, soil moisture plays a critical role in influencing the vegetation spectral profile throughout the growing season. These changes in feature status occur throughout the crop life cycle, posing challenges for remote sensing estimations of soil salinity. Data-driven modeling strategies depend on high spatial resolution and accuracy of auxiliary variables. Although numerous studies have incorporated a wide array of environmental variables--including soil texture, soil moisture, evapotranspiration, air temperature, soil temperature, and rainfall--the precision of the characteristics represented by these variables remains questionable. The data in question originate from various research organizations employing diverse production methods and strategies, with most exhibiting coarse spatial resolution. Furthermore, the digital characterization of anthropogenic management factors has yet to be integrated into the modeling process. In agricultural production, regularly applied nutrients (e.g., nitrogen, phosphorus, potassium) can indirectly alleviate the detrimental effects of salinity on vegetation. The volume of irrigation and the quality of water determine the salt input into agricultural land. Direct observation of changes in the soil is challenging due to vegetation cover, necessitating indirect assessments through observations of vegetation. Therefore, the quantification of these factors with high precision using remote sensing technology remains a pressing issue that current research must address.