## 6 Uncertainty and Challenge  
### Uncertainty in Machine Learning Modeling  
Studies utilizing ML commonly implement a hyperparameter tuning strategy that either evaluates all necessary hyperparameters or employs a randomly selected combination of them. The hyperparameter yielding the lowest uncertainty (e.g., RMSE) is subsequently chosen to estimate soil salinity. It is crucial to recognize that these hyperparameters are specific to the site, crop conditions (i.e., phenophase), and species [98]. Moreover, minor deviations in the site due to variations in crop, climate, and environmental conditions may lead to increased uncertainty in the search parameters. This consideration becomes particularly significant when ML models are applied to new regions or locations where crop types, soil conditions, climate factors (i.e., temperature, relative humidity, and rainfall), and environmental aspects (i.e., soil type, soil moisture, and topography) may differ from those of the original training locations. Consequently, there is a need for a robust transferability framework that accommodates changes in crop types, physiological conditions, and climate and environmental factors to enhance ML transferability and minimize the necessity for extensive field data collection.  
Satellite and aerial images are susceptible to variations in atmospheric composition on the day of acquisition, and residual errors persist even after atmospheric correction. Air pollution, coupled with the limitations of atmospheric correction (AC) tools to fully mitigate its effects, can contribute to increased uncertainty in the accuracy of estimates related to crop and soil parameters [142]. Changes in atmospheric conditions during image acquisition have posed challenges since the advent of satellite-based remote sensing. Aerosols--such as smoke, dust, and atmospheric gases like CO\({}_{2}\)--enhance the backscatter signal while diminishing the surface-directed reflection signal [143], thereby compromising the radiometric integrity and consistency of reflectance measurements. Consistent radiometric measurements are crucial for reliable estimation of soil parameters.  
Although the high spatial resolution of sentinel series data presents significant opportunities for soil salinity retrieval, it may also introduce uncertainties that impact the accuracy of soil salinity estimates derived from various retrieval techniques [48]. These uncertainties arise from the high spatial variability associated with background features such as soil composition, vegetation, dead branches, and shadows between test lines. Such features can interfere with the retrieval algorithm, particularly if their spectral characteristics are not accounted for during model calibration, resulting in false positives. Consequently, masking these background features is a crucial preprocessing step.  
Obtaining high spatial resolution data (such as PlanetScope) also presents challenges in image registration, which can result in resource wastage. This issue arises because the accuracy of most Global Positioning System (GPS) devices is lower than the pixel size of ultra-high resolution systems. To mitigate this problem, [PERSON] et al. [144] proposed that each image be analyzed as a separate plot for monitoring purposes. Furthermore, research conducted in China can utilize hybrid GPS-Beidou series satellite handheld positioning equipment, which can reduce ground measurement errors from 5 m (when using GPS satellites alone) to 2 m.