## 3 Results and Discussion
### Impact of Vegetation Cover on Microwave-Based Soil Moisture Estimation  
Microwave remote sensing, particularly in the active (e.g., SAR) and passive (e.g., radiometers) domains, has been widely used to estimate SM due to its direct sensitivity to the dielectric properties of soil, which are strongly influenced by moisture content [208]. Microwave sensors are highly sensitive to surface roughness, vegetation cover, and topographic variations, which affect backscatter and emissivity. In flat and sparsely vegetated areas, microwave signals can penetrate the soil surface and provide accurate estimates of SSM [209]. However, in hilly or mountainous terrain, topographic effects lead to distortions such as foreshortening, layover, and shadowing in SAR data, degrading the accuracy of SM retrievals [210]. Vegetation cover reduces the performance of ML models in SAR-based SM estimation by attenuating and scattering SAR signals, making it difficult to isolate the SM contribution [211]. Dense vegetation increases volume scattering, leading to higher backscatter values that do not directly correlate with SM. Several studies have shown the impact of vegetation cover on model performance. [PERSON] et al. [99] estimated SM in the lower Colorado River Basin, USA, where the site ranged from low to dense vegetation cover. Their study used backscatter and incidence angle, along with NDVI. The results showed that an increase in vegetation density adversely affects the performance of ML models in SM estimation. The RMSE increased by approximately 75% when moving from low vegetation to dense vegetation conditions. [PERSON] et al. [67] estimated SM across 103 sites across the globe with diverse land covers, including croplands, grasslands, shrublands, forests, and mosaic landscapes (a mixture of trees, shrubs, herbaceous vegetation, and croplands). Their study utilized data from Sentinel-1, Sentinel-2, SMAP, as well as topographical and climatic variables. The results showed that ML models performed the worst under mosaic land cover (MAE of 0.05 cm\({}^{3}\)/cm\({}^{3}\)), while the best performance was observed in shrublands (0.02 cm\({}^{3}\)/cm\({}^{3}\)). The MAE in mosaic land is nearly 150% higher than MAE in shrublands. The shrubland class is sparsely vegetated, allowing SAR signals to effectively interact with the land surface. In contrast, forests and mosaic land covers posed challenges, as SAR signals struggle to penetrate dense vegetation and become scattered, making SM estimation in these areas more difficult. Similarly, [PERSON] et al. [169] used RADARSAT-2 features and ML models to estimate SM across various land cover types in Turkey. The study found that the models performed well in sparsely vegetated areas (R\({}^{2}\) of 0.80), but their performance declined in densely vegetated areas (R\({}^{2}\) of 0.74). This decrease in performance was attributed to the scattering effect of the SAR signal caused by dense vegetation, which reduces the correlation between backscattering coefficients and SM content. [PERSON] et al. [64] estimated SM at different phenological stages of wheat (emergence, tillering, overwintering, and standing) in China using ML models. Backscattering coefficients from Sentinel-1 and vegetation indices from Sentinel-2 were used as input features for the models. The results revealed that the performance of the ML models declined as the NDVI of wheat increased. The best performance of the ML models was observed during the emergence stage of wheat (low NDVI), while the worst performance was observed during the standing stage (high NDVI). This decrease in performance can be attributed to the dense vegetation at higher NDVI values, which limits the ability of SAR signals to penetrate the canopy and retrieve accurate SM information. Similarly, [PERSON] et al.