Improved Modeling of Vegetation Phenology Using Soil Enthalpy.

Many vegetation phenological models predominantly rely on temperature, overlooking the critical roles of water availability and soil characteristics. This limitation significantly impacts the accuracy of phenological projections, particularly in water-limited ecosystems. We proposed a new approach incorporating soil enthalpy-a comprehensive metric integrating soil moisture, temperature, and texture-to improve phenological modeling. Using an extensive dataset combining FLUXNET observations, solar-induced fluorescence (SIF), and meteorological data across the Northern Hemisphere (NH), we analyzed the relationship between soil enthalpy and vegetation phenology from 2001 to 2020. Our analysis revealed significant temporal trends in soil enthalpy that corresponded with changes in leaf onset date (LOD) and leaf senescence date (LSD). We developed and validated a new soil enthalpy-based model with optimized parameters. The soil enthalpy-based model showed particularly strong performance in autumn phenology, improving LSD simulation accuracy by at least 15% across all vegetation types. For shrub and grassland ecosystems, LOD projections improved by more than 12% compared to the temperature-based model. Future scenario analysis using CMIP6 data (2020-2054) revealed that the temperature-based model consistently projects earlier LOD and later LSD compared to the soil enthalpy-based model, suggesting potential overestimation of growing season length in previous studies. This study establishes soil enthalpy as a valuable metric for phenological modeling and highlights the importance of incorporating both water availability and soil characteristics for more accurate predictions of vegetation phenology under changing climatic conditions.