## 4 Conclusions  
This analysis aims to systematically evaluate a range of ML studies applied to the estimation of SM across diverse land covers and geographic regions. Use of ML approaches offers a promising alternative to conventional empirical and physics-driven models, which require extensive biophysical parameterization. Our analysis extends from traditional ML techniques, including support vector machine (SVM) and tree-based models, to the incorporation of advanced multi-classifiers, ensemble frameworks, and state-of-the-art DL models. These recent developments have significantly improved the accuracy of SM predictions at various depths through the application of RS technologies. Many ML algorithms (Figure 5) were employed to estimate SM using RS datasets. Based on the model performance mentioned in the studies, RF (40 out of 67 times), SVR (13 out of 39 studies), and ANN (12 out of 27 times) often outperformed other algorithms. The selection of input features in ML models depends on whether SSM or RZSM is the focus. All 144 studies reviewed here used a range of input features, including vegetation indices derived from optical sensors, SAR features from microwave sensors, soil physical and hydraulic properties, and topographical features. Some features were utilized more than others. For SSM estimation (out of 119 studies), NDVI (60 studies), elevation (40 studies), and backscattering coefficients (VV: 32 studies and VH: 27 studies) were the most common predictors. For RZSM estimation (out of 25 studies), SSM (13 studies) and NDVI (13 studies) were the most common predictors. Per this synthesis (Section 3.4.1), for SSM estimation, satellite-derived features such as vegetation indices and backscattering coefficients provide critical information on SSM variability. In contrast, satellite observations alone were insufficient to capture RZSM variability. Soil physical and hydraulic properties, along with SSM, were identified as key predictors of RZSM. Integrating multi-source data consistently enhanced the performance of ML models in SM estimation. Multi-source data includes satellite and other remote sensing products (Sentinel-1, MODIS, Sentinel-2, Landsat, SMAP, and UAV) to derive input features to estimate SM. Though identifying the best in situ SM method for ground-truth data is out of the scope of this review, we note that only 80 studies out of 144 reported the methods used to measure in situ SM. We advocate that reporting of the method used to collect SM training data is essential to assess RS method performance. Gravimetric (24 out of 80 studies) and TDR (18 out of 80 studies) methods were most frequently used (refer to Section 3.7). Despite multiple advantages of SM estimation using ML and RS techniques, transferability of models trained at one site to new sites remains a major challenge (Section 3.6). Studies (Section 3.5) also identified that vegetation cover affects SAR-based SM estimation, particularly in the X- and C-bands, and improved performance was observed when removing vegetation effects on SAR signals using semi-empirical models. Hybrid ML models (combining two or more ML models: Section 3.1) were investigated and found to improve SM estimation accuracy, reducing the bias-variance tradeoff. Future research should prioritize exploring and validating these potential approaches (use of semi-empirical models, hybrid ML, and Transformer) to effectively address the challenges identified and overcome the existing limitations.