As an important component of forest ecosystem processes, leaf litterfall plays a key role in nutrient cycling and ecosystem functioning. However, monitoring litterfall dynamics in subtropical forests remains challenging due to complex community structures and asynchronous leaf phenology, which limit the applicability of remote sensing approaches developed for temperate forests. As a critical linkage between vegetation and soil carbon pools, leaf litterfall directly influences forest carbon sequestration by providing carbon inputs in the form of litter. Unlike the concentrated autumn leaf fall in temperate forests, subtropical forests exhibit complex community structures with concurrent leaf abscission and new leaf growth, limiting the applicability of temperate-focused remote sensing techniques. To address this, we collected annual leaf litterfall data from 18 plots in eastern China’s subtropical forests and integrated these with high-resolution Sentinel-2 imagery using supervised machine learning models to develop a novel monitoring method. Our results indicated that subtropical forests exhibited clear seasonal leaf litterfall peaks during spring, summer, and autumn. Sentinel-2 satellite imagery combined with supervised machine learning algorithms can effectively monitor forest leaf litterfall dynamics. Temporal models, which use multi-date monthly spectral differences (R2adj = 0.70, RMSE = 0.46, RPD = 1.86), significantly outperformed instantaneous models based on single-date canopy states (R2adj = 0.33, RMSE = 0.85, RPD = 1.24). Following variable selection, model performance improved, with R2 increasing by more than 2% in most models and the number of variables reduced by over 44%. Robustness analysis indicated that the model was spatially robust (no significant bias among sites), and despite seasonal intercept differences, the slopes were consistent, enabling reliable tracking of litterfall dynamics. Among the examined spectral indices and canopy characteristics, those reflecting canopy greenness, pigments, and structure contributed over 65%, with WV-VI, MCARI2, and LAI being most influential. Incorporating drought-sensitive water indices and soil exposure-related mineral indices further enhanced model performance. These indices may partially reflect drought stress or seasonal canopy opening. Our findings provide a new method for monitoring leaf litterfall dynamics in structurally complex subtropical forests and offer a critical theoretical basis for accurately assessing leaf fall dynamics. Our findings provide a novel and effective method for monitoring leaf litterfall dynamics in structurally complex subtropical forests, improving seasonal litterfall assessment and supporting vegetation monitoring, with potential implications for ecosystem- and carbon-related studies.
Xie et al. (Sat,) studied this question.