## 6 Uncertainty and Challenge
### Challenges in Data and Methods  
Moisture, organic matter, ion content, roughness, parent material, climatic factors, and other variables can all influence the spectral response of soil surfaces affected by salt content [1; 24]. In particular, moisture and organic matter significantly impact the remote sensing inversion results of salt content. To minimize the influence of environmental variables on soil salinity predictions, the selection and accuracy of auxiliary variables are crucial. When choosing auxiliary variables, it is important to select those most closely related to the target variables, considering the characteristics of the study area and the changes in the target variables during the study period. This approach aims to achieve optimal training effectiveness for the ML model. In addition to the type of auxiliary variables, the source and accuracy of the datasets for both auxiliary and target variables contribute to the uncertainty of results. Firstly, the accuracy of the target variable itself is vital. Both model training and the evaluation of final predictions are closely linked to the accuracy of the target variable. Since the target measurement directly participates in the model training process, any lack of precision in the target measurements undermines the reliability of the relationship between the fitted and auxiliary variables. Secondly, the accuracy of the auxiliary variable datasets is critical. As the explanatory variables for the target variables, auxiliary variables form the core of the training model. Their accuracy significantly affects the model's prediction results by influencing the model's construction. To enhance the prediction accuracy of ML algorithms, it is essential to ensure that the training samples possess sufficient representativeness. When constructing training models over small spatial scales, it is important to ensure that the training data encompasses a sufficiently long time series and covers typical land surface variation periods. Specifically, training samples should account for high values while also encompassing the middle and low-value regions of the target variables as much as possible. Additionally, consideration should be given to variations in terrain, land type, climate, and auxiliary variables. When building training models on a larger scale or over extensive areas, it is necessary not only to ensure that training samples cover high, middle, and low-value areas of the target variable but also to consider the sufficient spatiotemporal representativeness of the foundational data. More importantly, many inversion models based on ML algorithms are constructed using point-scale measured data and pixel-scale auxiliary data. Most studies neglect to address the impact of scale differences between sample data on parameter inversion. Under relatively uniform surface conditions, the scale difference may be negligible. However, since no surface is absolutely uniform, the scale difference between measured data and auxiliary data can diminish the accuracy of inversion results derived from ML to some extent. To enhance the precision of inversion results, it is advisable to obtain ground observations that are similar in scale to the auxiliary variables when constructing training samples. Given that ground-based observations typically cover a small area, scaling ground-based \"point\" observations up to the pixel scale is necessary. Soil visible-near infrared spectroscopy (V-NIR) is significantly affected by environmental factors such as water content and multi-parameter coupling. The study suggests several methods to mitigate the moisture effect on spectra, including direct standardization (DS [145]), piecewise direct standardization (PDS) [146], and external parametric orthogonalization (EPO) [147]. EPO was first introduced by [PERSON] et al. [148] to correct moisture effects in the spectra of wet soil samples. Following EPO treatment, the prediction accuracy of the soil organic carbon (SOC) prediction model significantly improved for the spectra of wet topsoil samples.