## 3 Results and Discussion
### Important Features
#### 3.4.1. SSM and RZSM Prediction  
The 144 papers reviewed focused on either SSM estimation (119 studies, Table 2) or RZSM estimation (25 studies, Table 3). SSM studies most frequently used the normalized difference vegetation index (NDVI) feature (60 studies), followed by elevation (40 studies) and backscattering coefficients (VV in 32 studies, VH in 27 studies) (Figure 7a). Notably, soil properties did not rank among the top 10 features. RZSM studies most frequently used SSM and NDVI (13 studies each), followed by precipitation, land surface temperature (LST), enhanced vegetation index (EVI), and soil properties (sand, silt, and bulk density) (Figure 7b).  
Figure 7: Top 10 input features used in machine learning-based soil moisture estimation. This is derived from Tables 2 and 3. (**a**) Surface soil moisture, (**b**) root zone soil moisture, (**c**) field-scale studies, and (**d**) global/regional-scale studies. We defined the field scale as studies focused on specific locations or individual fields, the regional scale as studies covering particular countries, states, or regions, and the global scale as studies examining multiple countries or worldwide.  
Few studies have systematically analyzed feature importance to identify the most sensitive variables within a given set of input features. We report the most influential features by marking them with an asterisk (*) at the end of features in the input features column of Tables 2 and 3. For SSM, vegetation indices and backscattering coefficients were found to be the most significant predictors. [PERSON] et al. [110] estimated SSM using RADARSAT-2 in agricultural fields (corn, soybean, and winter wheat) in Canada and observed that backscattering coefficients (VV and VH) played a critical role. Backscattering coefficients establish a direct relationship between radar signals and SM content. As SM increases, the dielectric constant rises, enhancing signal reflection and backscatter intensity. Conversely, dry soil lowers the dielectric constant, reducing backscatter. This sensitivity makes backscattering coefficients reliable indicators of SM variability [190]. Similarly, [PERSON] et al. [165] assessed SSM in vegetated floodplains in Germany using Sentinel-1 and Landsat data, identifying backscattering coefficients and NDVI as the most important features in ML models. Other studies conducted across various regions have also reported that vegetation indices, backscattering coefficients, and topographical features are key predictors for SSM estimation [112; 145; 159].  
In contrast, for RZSM, soil properties and SSM play a more significant role than backscattering coefficients and vegetation indices. [PERSON] and [PERSON] [95] estimated multi-layer SM (up to 100 cm) across CONUS and found that soil properties were crucial for predicting deeper-layer SM. [PERSON] et al. [97] estimated RZSM (up to 80 cm) and identified soil properties (sand and clay content), topographical variables (slope and aspect), and LST as key influencing factors. Similarly, [PERSON] et al. [86] estimated RZSM in wheat fields in Arizona, USA, and reported that SSM and soil properties were the primary determinants. Studies conducted in various regions have consistently indicated that SSM, soil properties, and topographical features are more influential than vegetation indices and backscattering coefficients in RZSM estimation [83; 191; 192]. Satellite-based features, such as vegetation indices and backscattering coefficients especially from X and C-bands SAR, could not provide information on deeper SM due to their inability to penetrate to a deeper profile [34; 40; 193]. SSM serves as an essential proxy for deeper SM content due to its strong hydrological connectivity with subsurface layers [194; 195], while soil physical and hydraulic properties control water retention, infiltration, and redistribution processes, significantly influencing SM availability in the root zone [86; 97; 196].