Bridging the gap between remotely sensed phenology and the underlying ecophysiological processes: The SWELL model

Abstract Vegetation phenology studies the periodic recurrence of plant life‐cycle events and is essential for understanding ecosystem responses to environmental changes. Remote sensing has become a key tool for monitoring phenological events on large spatial and temporal scales, primarily using vegetation indices like the Normalized Difference Vegetation Index (NDVI). However, current methods for extracting phenological metrics from NDVI data often fail to capture their biological and physiological significance, as they are predominantly based on statistical fitting functions. This study presents SWELL (Simulated Waves of Energy, Light, and Life), a process‐based phenology model that simulates the temporal NDVI profile, from leaf unfolding to dormancy release, based on species‐specific photothermal response functions. Tested on European beech, SWELL successfully reproduced seasonal Moderate Resolution Imaging Spectroradiometer NDVI patterns from 2012 to 2021 across ecoregions, matching the performance of a benchmark model and enabling consistent analysis of phenological phases' timing across biogeographic gradients. SWELL allows bridging the gap between remotely sensed phenology and the underlying ecophysiological processes. By overcoming current limitations in process‐based phenology modelling, SWELL may represent a novel tool for understanding and predicting vegetation phenology in the context of climate change.