## 3 Results and Discussion
### Important Features  
The choice of input features in ML models plays a crucial role in determining the accuracy of SM estimation. Commonly used input features include backscattering coefficients, incidence angles, and brightness temperatures from microwave sensors; vegetation and thermal indices from optical sensors; climatic variables; soil properties; and topographic variables (Tables 2 and 3). The selection of these variables largely depends on the scale of the study (global/regional or field scale) and the soil depth being considered (surface or root zone). We examined which input features are frequently used in SSM estimation, RZSM estimation, and across different scales (global/regional and field scale). Figure 7 illustrates the top 10 features used in SM estimation using ML models across different scales and soil depths. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{[PERSON] et al. [122]} & \multirow{2}{*}{China, Regional} & \multirow{2}{*}{China, Regional} & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \ \begin{tabular}{} \end{tabular}  &
\begin{tabular}{} \end \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline [PERSON] et al. [143] & India, Regional & LR, MLR, RF, KNN, SVM & Sentinel-1, Sentinel-2 & VV \({}^{\star}\), VH & Over drying & 5 & RF \\ \hline [PERSON] and & India, Field & LR, KNN, RF, DT, SVR & UAV & NDVI, NDWI, TNDVI, Simple Ratio or & Gravimetric & 1, 5 & LR, SVR \\ \hline [PERSON] et al. [73] & India, Regional & DMN-Bi-GRU & Sentinel-1, Sentinel-2 & NDVI, GLAL, GNDVI, WDWI & - & SSM & DMN-Bi-GRU \\ \hline [PERSON] and [PERSON] [145] & India, Regional & RF, SVN, GP & Radar Imaging & & & & & & \\  & & & Satellite 1 & HH \({}^{\star}\), HV, VH, VV, sand, silt, clay & Gravimetric & 5 & GP \\ \hline [PERSON] and & & & ANN, GCNN, RBN, ERBN, & & Sentinel-1, & & & & \\  & & GPR, SVN, RF, Boosting, RNN, & Sentinel-2 & VV, VH, VH/VV, AH-VV, angle, NDVI, elevation, & ML3 theta probe & 5 & ANN \\  & & & SNRDDT, & & & & & \\  & & AutoML, & & & & & & \\ \hline [PERSON] et al. [66] & India, Regional & LR, SVR, DT, RF, LSTM & SMAP & SSM, LST, soil texture, land cover, wind direction, U, & - & 5 & LSTM \\ \hline [PERSON] et al. [96] & South Korea, & & & & & & & & \\  & Regional & DNN & MODIS & ORL, Isolation accumulated precipitation (12 h, 24 h), NDVI, T\({}_{w}\) & SM probes & 10 & - \\ \hline [PERSON] et al. [146] & South Korea, & & & & & & & & \\  & Regional & & & & & & & \\ \hline [PERSON] et al. [146] & South Korea, & & & & & & & & \\  & Regional & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline [PERSON] et al. [92] & Iran, Regional & & & & & & & & \\  & & & ANN, EN & Landsat 7, 8 & LST \({}^{\star}\), blue \({}^{\star}\), green, red, NII \({}^{\star}\), SWIR1, SWIR2 & Capacance & 10 & RF \\ \hline [PERSON] et al. [74] & Iran, Regional & GBM, SVR, & GLDAS, & AMSE-2, AMSE-2, ASME-X, SMAPL3, & TDR & 6 & GBM- \\  & & GBM-SVR & AMSE-3, SMAPL, GLDAS\({}^{\star}\) & & SMAPL, GLDAS\({}^{\star}\) & & & \\ \hline [PERSON] et al. [147] & Iran, Regional & & RF, SVR, XGBost, Extra tree & Sentinel-2, Landsat 8 & Salinity index -81, Salinity index -85, SND1 \({}^{\star}\), NDVI, SAAT, VSSI, & Oven drying & 10 & Extra tree \\ \hline Fathboloumi & & & & & & & & \\  & & & & & & & \\ \hline Fathboloumi & & & & & & & & \\ \hline Fathboloumi & & & & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [148]} & Iran, Regional & RF, Cubist & Landsat 8 & Elevation, aspect-cos, catchment area, & & & & \\  & & & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [148]} & \multirow{2}{*}{Iran, Regional} & \multirow{2}{*}{RF, Cubist} & \multirow{2}{*}{ Landsat 8} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{SSM} & RF \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2. Cont.