Global Rooting Depth Inferred based on Machine Learning

Rooting depth Z_r is a key variable controlling plant water uptake, soil–vegetation interactions, and land–atmosphere feedbacks. Despite its importance, global estimation of Z_r remains challenging due to sparse in situ observations and strong spatial heterogeneity driven by climatic, edaphic, and vegetation controls. The interaction among these factors increases complexity, limiting the performance of traditional process-based models and leading to substantial uncertainty in large-scale applications. In this context, machine learning offers a data-driven alternative that can integrate heterogeneous datasets and capture nonlinear relationships and complex interactions among environmental variables, providing a flexible framework for improving large-scale estimates of rooting depth.

In this research, we investigate the environmental drivers of rooting depth at the global scale and develop a new spatially explicit Z_r dataset using advanced machine learning methods. Our framework integrates multiple globally consistent datasets, including satellite-derived vegetation metrics (LAI, NDVI), land-surface temperature, and gridded climate variables (precipitation, radiation). These are complemented by soil hydraulic and physical attributes from global soil databases and detailed topographic information, providing a complete representation of environmental controls relevant to rooting depth. A Random Forest model is employed to capture the nonlinear relationships between the predictor set and observed rooting depths. Model interpretability is subsequently assessed using Shapley Additive exPlanations (SHAP), thereby quantifying the contribution of each environmental variable to model predictions.

The optimized model is subsequently applied at the global scale to generate a global Z_r dataset using globally available plant, soil, and climate variables. By accounting for their combined effects, the model provides a spatially continuous representation of rooting depth across diverse regions. Model performance is evaluated using leave-one-out cross-validation (LOOCV), whereby each observation is iteratively excluded from the training dataset and used for independent validation. In addition, the resulting predictions are compared against existing global rooting depth datasets to evaluate large-scale consistency. The new Z_r dataset enables improved drought monitoring capabilities through more realistic estimates of plant available water; it may enhance water resource assessments by refining infiltration and groundwater recharge estimates, and it helps reduce uncertainty in land surface and climate models by better representing soil-vegetation interactions. Overall, this work provides a robust data-driven approach for estimating Z_r globally, independent of process-based assumptions, and relevant for diverse ecohydrological applications striving towards more accurate characterizations of terrestrial water and carbon cycling.

Keywords: rooting depth, machine learning, soil vegetation interactions, global hydrology, ecohydrology, Earth system modeling