Trade-offs between data-driven and process-based approaches for root-zone soil moisture retrieval in a Mediterranean vineyard

Accurate monitoring of water availability in the root zone is a prerequisite for generating precise irrigation recommendations and mitigating drought impacts in water-limited Mediterranean ecosystems. This work evaluates the performance and physical consistency of two distinct modelling paradigms to retrieve Root-Zone Soil Moisture (RZSM) in a vineyard located in Terra Alta (Catalonia, Spain), intended as a basis for operational decision support.We contrast a purely data-driven method, utilizing a Multilayer Perceptron (MLP), against a process-based approach that couples a parsimonious multilayer soil model with an Ensemble Kalman Filter (EnKF) for the assimilation of Surface Soil Moisture (SSM). Both schemes are currently benchmarked using in-situ SSM observations and standard meteorological forcing.The results highlight a clear dichotomy between predictive skill and physical interpretability. The neural network approach demonstrated excellent performance in capturing non-linear seasonal trends and rapid wetting events, yielding better Kling–Gupta Efficiency (KGE) scores during validation. Conversely, the physical model exhibited lower statistical metrics but ensured mass conservation and provided a transparent representation of vertical water transport.We conclude that while machine learning excels in reproducing local dynamics, the physical framework offers the robustness required for consistent water accounting. Consequently, we propose a synergistic roadmap where machine learning is leveraged to regionalize model parameters, and the assimilation of high-resolution satellite Surface Soil Moisture serves to spatialize the state estimates. This integration is essential to scale up from plot-level findings to regional irrigation recommendations, supporting the next generation of Digital Twins in agriculture.