## 3 Results and Discussion
### Suitability of ML Models in SM Estimation  
Among the various ML models applied in SM estimation using RS data (Figure 5), random forest (RF) was the most frequently used model (67 studies, 47%), followed by support vector regression (SVR, 39 studies, 27%) and artificial neural networks (ANNs, 27 studies, 19%). Similarly, RF was most often reported as the best model (40 studies, 28%) followed by SVR (13 studies, 9%) and ANNs (12 studies, 8%). Other classical ML models (20 out of 55 studies), other neural networks (20 out of 36), and other neuro-fuzzy models (6 out of 10), all listed in the figure caption, were often reported as the best-performing models in the respective studies. In some studies, multiple models performed equally well, leading to ties. In such cases, all tied models were counted as best-performing models. Additionally, some studies relied on a single model from their preliminary analysis. The performance metrics of each model can be found in Supplementary Table S1. RF was the best-performing and most widely used model for SM estimation. Among neural network (NN) models, ANNs emerged as the most effective algorithm, achieving substantial recognition for their accuracy and reliability. Studies have used various performance metrics (R\({}^{2}\): coefficient of determination, RMSE: root mean squared error, and MAE: mean squared error) to evaluate the ML models in SM estimation. This review synthesizes model performance metrics (e.g., R\({}^{2}\), RMSE, MAE) as reported in the original studies without recalculating them under a unified framework. As such, differences in R\({}^{2}\)/RMSE calculation methods may introduce inconsistencies when comparing results across studies. This methodological heterogeneity should be considered when interpreting the reported model performance. Classical ML models (RF, SVR, and XGBoost) are most effective when working with structured, tabular datasets derived from RS indices and ground-based measurements [57; 58; 59; 60]. Their simplicity, interpretability, and robustness to noise make them well-suited for scenarios where the spatial and temporal complexity of the data is relatively low. ANNs, while also operating on tabular inputs, are better equipped to capture nonlinear interactions among diverse input features, such as those from combined climatic, soil, and vegetation data sources [61; 62]. CNNs, on the other hand, are particularly advantageous for image-type datasets (such as multispectral or radar imagery) due to their ability to extract spatial features automatically through convolutional layers [63; 64; 65]. This makes CNNs well-suited for tasks where spatial patterns (e.g., texture, land cover variation) are important. LSTM networks outperform other models in time-series applications by capturing long-range  
Figure 4: Temporal evolution in coverage of surface soil moisture (SSM) and root zone soil moisture (RZSM) estimation using machine learning (ML) in 144 papers (2010–2024) synthesized in this study. temporal dependencies, making them ideal for predicting SM based on historical sequences of weather or vegetation indices [66; 67; 68]. Traditional single ML models often struggle to capture the complex variability of SM. One major limitation of single ML models is the bias-variance tradeoff. For example, linear regression-based models tend to have high bias, potentially oversimplifying the relationship between predictors and the target variable. In contrast, decision tree-based and NN models are often high-variance, making them prone to overfitting. To address these limitations, recent studies have explored hybrid models, which combine multiple ML models to take advantage of their respective strengths while mitigating their weaknesses. [PERSON] et al. [69] compared the performance of individual models, such as RF, SVR, and Backpropagation Neural Network (BPNN), against a hybrid approach that combined these models using ordinary least squares techniques.