## 3 Results and Discussion
### Feature Importance Analysis  
To better understand the influence of meteorological features and soil depth on SMC prediction in the two examined soil textures (loam and silt loam), as well as to improve the model's interpretability, feature importance analysis was performed for both soil textures using the GBR model. The results reveal that the impact of the analyzed variables varied with soil texture. In loam soil, precipitation and humidity were the most influential features in prediction, with values of 31% for both (Figure 6). Soil depth had a substantial impact on SMC, with an importance value of 27%, as loam soil retains water effectively due to its balanced particle size distribution. Precipitation and humidity also played crucial roles in influencing SMC in loam soil. In contrast, wind speed and solar radiation had minimal impact, both with importance values below 6%. These results emphasize the significance of considering precipitation, humidity, and soil depth when monitoring and predicting SMC in loam soils, particularly as precipitation directly contributes to replenishing soil moisture [47], and humidity indirectly affects soil air water dynamics [48]. Conversely, in silt loam soil, the results emphasize the importance of solar radiation in SMC prediction with a value of 46%, followed by wind speed and depth with values of 26% and 24%, respectively, highlighting the role of radiation-driven evapotranspiration in controlling moisture variation in silt loam soils due to their balanced texture and its high water-holding capacity compared with loam soil and the role of wind in increasing evapotranspiration.  
These findings are consistent with previous research, highlighting the critical role of environmental variables such as precipitation and solar radiation in influencing soil moisture dynamics and demonstrating the complexity of soil-water interactions in the studied soils [49]. Additionally, integrating the effects of soil texture and depth provided further insights into their significance in driving the fluctuations and dynamics of SMC [29].  
The results also emphasize the importance of incorporating multiple features in model training for SMC prediction across different soil textures, including vegetation indices, which offer valuable information on soil-plant interactions and their impact on soil moisture patterns [19; 50]. Increasing the number of sampling locations across the field would further improve the spatial representativity of the dataset, thereby enhancing the model's robustness and generalizability. The model effectively captured the relationships between the selected input variables and SMC in both soil textures, highlighting its potential to reduce the need for extensive field sampling and support cost-efficient soil moisture monitoring.  
Figure 5: Evaluation metrics (R\({}^{2}\), MSE, RMSE, MAE, and NSE) for soil moisture content prediction in loam and silt loam soils using the GBR model.  
These findings show the complex interactions of meteorological conditions and depth with SMC [51]. In contrast, satellite-based models frequently have higher spatial resolution but limited soil penetration and temporal alignment with ground conditions, since they utilize passive microwaves signals and downscaling approaches to predict SMC in the surface soil layers [52]. These findings highlight the significance of soil-specific water management systems and real-time monitoring to improve crop production precision.