## 4 Discussion
### Limitations and Uncertainties of the Models  
The statistical correlations obtained in the models present promising results, demonstrating a significant relationship between satellite-derived spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index). However, it is essential to highlight some inherent limitations of these indices and the resulting classifications, which can introduce uncertainties in the analysis and interpretation of the data [63].  
One of the main limitations of spectral indices lies in their dependence on prevailing climatic conditions, particularly temperature and precipitation. As noted by [64], NDVI is highly sensitive to climatic interactions, implying that changes in temperature and precipitation regimes, such as those resulting from climate change, can significantly affect its outcomes. Therefore, further investigation is needed to understand how NDVI responds to these changes, especially under future climate scenarios.  
Moreover, the values of spectral indices may vary among different sensors due to factors such as quality control measures, algorithm implementation, and atmospheric and geometric corrections of the sensor used [65]. This variability can introduce uncertainties when comparing NDVI and NDWI indices over time and across different regions. Studies by [64; 66] also indicate that factors beyond climatic influences, such as the presence of wetlands around water bodies, irrigation practices, and anthropogenic activities, can affect vegetation growth and health. These factors are crucial when analyzing spectral indices, as they may confound the interpretation of the results obtained.  
Another source of uncertainty involves the calibration of models used to estimate soil moisture from NDWI, particularly in transition areas between wet soil and vegetation and in regions with steep topography, where the so-called \"background effect\" occurs [65; 67]. In these areas, the probability of error increases due to spectral confusion, potentially compromising the accuracy of soil moisture estimates. Therefore, although NDVI and NDWI indices exhibit high correlations, the uncertainties inherent in data collection and modeling processes need to be considered when interpreting results. This is crucial to avoid the inappropriate extrapolation of results to other areas or environmental conditions and to adequately contextualize study conclusions.  
Indices based on reflectance rates tend to minimize the effects of sensor calibration, as well as bidirectional, atmospheric, and topographic effects. However, they are still sensitive to factors such as the background effect and the scale of analysis [65]. Such limitations can lead to misinterpretations when applying indices to areas with different characteristics from those used to calibrate the models.  
Recognizing these limitations and uncertainties associated with satellite indices is essential for a more critical and well-founded analysis of results, allowing for a more robust discussion of the relationships between soil moisture, vegetation cover, and surface runoff. In this way, we can not only improve the quality of the inferences made but also ensure that our conclusions are appropriately contextualized, considering the uncertainties of the methods employed.