## 3 Results and Discussion
### ML Model Transferability Challenges and Solutions  
Transfer of ML models trained in one geographic location (which may have unique climate, land cover, soil types, and environmental conditions) to new locations offers the ability to reuse learned patterns, minimizing the need for extensive retraining, and enabling practical application in regions where local data may be scarce or expensive to collect. Several studies have evaluated the transferability of ML models for SM estimation and observed that models often face challenges in accurately capturing SM dynamics when applied to new locations with differing environmental conditions. [PERSON] et al. [165] trained SVR models using Sentinel-1 derived features at two separate sites in Germany (Elbe and Peene) and tested their transferability by applying the model trained at one site to the other site. When the model trained at Peene was tested at Elbe, it produced a low R\({}^{2}\) of 0.41 and a high root mean square error (RMSE) of 47%. Similarly, the model trained at Elbe and tested at Peene performed poorly, with R\({}^{2}\) of 0.12 and RMSE of 50%. However, when models were trained and tested within the same site, they performed significantly better, achieving R\({}^{2}\) of 0.86 with RMSE of 9.6% at Elbe and R\({}^{2}\) of 0.78 with RMSE of 11.6% at Peene. [PERSON] et al. [99] trained ML models using data from six sites in the Lower Colorado River Basin, USA, and tested their performance on four separate sites that were excluded from the training process. The results showed that the models struggled to accurately estimate SM at the test sites, performing significantly worse compared to models trained using data from all sites combined. [PERSON] et al. [213] estimated SM on a global scale using data from multiple networks within the International Soil Moisture Network (ISMN). The study found that models trained on data from a single network performed poorly when applied to different geographic locations. This result illustrates transferability issues for ML models across regions. To overcome the model transferability issues, a few studies used transfer learning techniques. For example, [PERSON] et al. [109] estimated SM in Canada under various land-cover conditions and soil roughness using ML models. The models performed poorly when trained and tested on different phases, with RMSE exceeding 0.06 cm\({}^{3}\)/cm\({}^{3}\). To address this issue, the study employed various transfer learning techniques, including target domain adaptation, scattering models, and clustering models, achieving improved RMSE ranging from 0.032 to 0.045 cm\({}^{3}\)/cm\({}^{3}\). These findings suggest that future studies should incorporate transfer learning to enhance model transferability. [PERSON] and [PERSON] [63] applied the concept of transfer learning to estimate SSM and address the challenge of training deep learning (DL) models at the local level with limited data. They trained a DL model using ISMN data followed by a fine-tuned process using locally collected datasets. Theirresults showed that transfer learning improved R\({}^{2}\) by 34% and reduced the RMSE by 18% compared to models without transfer learning. Fine-tuning is generally applied in a context where local SM samples are limited but pre-trained knowledge from a larger domain (ISMN data) is available. This technique is particularly applicable when the source and target domains share similar features (e.g., input variables like SAR and NDVI), but the target domain has too few samples to train a deep model from scratch. It is most effective when the model's initial training can generalize well and only needs minor adjustments to adapt to new conditions. Another way to improve SM estimation accuracy is to train ML models using data from homogeneous climatic, soil, and land-cover conditions. ML models may struggle when trained on data from diverse climates, soil, and land-cover types, leading to increased uncertainty in predictions. [PERSON] et al.