## 4 Conclusions  
This study demonstrated that the Gradient Boosting Regressor (GBR) model is a reliable and accurate approach for predicting SMC in two distinct soil textures, loam and silt loam, using soil depth and meteorological data as input features. The GBR models achieved high prediction accuracy in both soil textures, with R\({}^{2}\) values exceeding 0.94, and the highest performance observed in silt loam soil. These findings emphasized the robustness and generalization capability of the GBR model in capturing nonlinear interactions between SMC, depth and meteorological parameters, despite the limited sample size and spatial coverage.  
The repeated measures ANOVA confirmed significant variation in SMC across depths, particularly in loam soil, where deeper layers exhibited higher variability. In contrast, silt loam soil exhibited higher overall SMC with more stable distribution across depths. The feature importance analysis provided further insights, revealing that precipitation and humidity were the most influential variables in loam soil (both contributing approximately 31%), while solar radiation was the dominant factor in silt loam soil (46%). Soil depth also had a significant effect on SMC prediction in both soil textures, emphasizing the need to consider depth-specific patterns in modeling and monitoring efforts.  
These findings underscore the importance of incorporating both meteorological variables and soil-specific characteristics in SMC modeling. The model's ability to achieve high accuracy with limited data supports its potential to reduce the need for extensive field sampling, contributing to more efficient data-driven decision making for improving water use efficiency and irrigation management, in alignment with SDG 6 goals on sustainable water use and ecosystem protection.  
Additionally, the study emphasizes the necessity of soil texture-specific modeling combined with depth-sensitive predictions and explainable feature analysis. However, the study acknowledges the limitation regarding the assumption of independence in statistical  
Figure 6: Feature importance analysis results of GBR model in loam and silt loam soils.  
analysis, which was addressed by applying a repeated measures design. Future research should further strengthen the modeling framework by incorporating more repeated sampling, integrating additional soil physical properties, and considering the impact of crop growth stages through remote sensing data to enhance the accuracy and applicability of SMC prediction models.  
Conceptualization, A.N. and T.A.; methodology, T.A. and A.N.; validation, T.A., M.N. and A.N.; formal analysis, T.A.; investigation, T.A.; data curation, T.A.; writing--original draft preparation, T.A.; writing--review and editing, A.N. and M.N.; visualization, T.A.; supervision, M.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets for this study would be available by the authors upon request.  
The research was supported by the EKOP-24-3-I-Sze-107 University Research Fellowship Program of the Ministry for Culture and Innovation from the source of the national research, development, and innovation fund, and by the \"Precision Bioengineering Research Group\" supported by the \"Szeechenyi Istvan University Foundation\". And by the Janos Bolyai Research Scholarship (Bo/00578/24) of the Hungarian Academy of Sciences.  
The authors declare no conflicts of interest.