## 3 Results and Discussion
### Uncertainty and Future Research Directions in SM Estimation  
Uncertainty in SM estimation using ML and RS arises from multiple, often interrelated sources, including input data quality, spatial and temporal scale mismatches, and methodological limitations. A fundamental source of uncertainty is the limited generalizability of ML models. Models trained under specific biophysical and climatic conditions may perform poorly when applied to regions with different vegetation types, soil textures, or land management practices [99; 165; 213]. This limitation is particularly pronounced in models that rely heavily on vegetation indices or microwave backscatter signals, as these inputs are sensitive to surface roughness, canopy structure, and seasonal dynamics, all of which vary across space and time. The quality of RS inputs further contributes to uncertainty. Satellite-derived products can be affected by atmospheric interference, sensor calibration errors, and cloud contamination, particularly in optical imagery [221; 222]. Another critical source of uncertainty lies in the in situ measurements used for model training and validation. Different SM sensors (e.g., TDR, FDR, gravimetric) have varying levels of precision, spatial representation, and depth sensitivity, and these differences introduce inconsistencies in the ground measurement data [214; 219; 220]. When such measurement errors propagate through the modeling process, they can significantly influence the predictive reliability of ML algorithms. Additionally, differences in spatial and temporal resolution across remote sensing datasets introduce uncertainty in SM estimation by causing mismatches between input features and ground truth data, leading to aggregation errors and misaligned observations that affect model accuracy and reliability [223; 224]. These combined sources of uncertainty underscore the need for careful model design, transparent data reporting, and improved integration strategies to enhance the robustness and transferability of SM estimation frameworks. Figure 8: Frequency of in situ soil moisture measuring techniques used in soil moisture estimation using remote sensing and machine learning models. TDR: Time-Domain Reflectometry; FDR: Frequency-Domain Reflectometry; Other techniques include Campbell Scientific water content reflectometers, Capacitance Probe, Decagon GS3, EC-5 Sensor, Electrical Resistance, Embedded soil moisture sensor, GPR, HOBO, Hygrometer, Neutron Probe, Portable Soil Moisture Meter, PR2 Profile Probe sensors, Probe-based and the core-based, Soil Water Balance Equation, SPADE, TEROS 12, TR-6D Soil Thermometer, WET-2 Probe. While substantial progress has been made in SM prediction using RS and ML, several critical avenues remain to be explored. Future studies should prioritize the development and application of hybrid models that combine multiple ML or deep learning architectures to reduce bias-variance tradeoffs and improve predictive accuracy across different soil depths and land covers. There is also a growing need for advanced multi-sensor data fusion strategies that leverage the complementary strengths of optical, microwave, thermal, and UAV-based datasets. Transfer learning and domain adaptation techniques offer promising solutions to address the widely observed issue of poor model generalization across geographic regions and climatic zones. In parallel, physics-informed machine learning frameworks, which integrate hydrological constraints and soil physics knowledge into data-driven models, can improve model interpretability and reliability, especially in data-scarce regions. Furthermore, explainability and uncertainty quantification remain underexplored in SM modeling; tools such as SHapley Additive exPlanations, Bayesian frameworks, and dropout-based uncertainty estimation can enhance model transparency and practical usability.