| 17 Aug 2026
Generating a 30 m resolution annual forest litterfall production dataset across China during 2000–2024
Abstract. Forest litterfall links aboveground vegetation dynamics with belowground biogeochemical processes and plays an important role in soil microclimate regulation, erosion protection, and wildfire risk assessment. However, long-term, fine-resolution maps of litterfall production remain limited. Here, we compiled litterfall observations from the Chinese Ecosystem Research Network and published literature, and developed a spatial matching scheme to account for coordinate uncertainty in linking field measurements with remote sensing predictors. We integrated Landsat-derived spectral features, climatic variables, and topographic factors into a Random Forest model to generate annual 30 m forest litterfall production maps for China from 2000 to 2024. The model performed well for independent testing samples, with an R² of 0.72, and the resulting 30 m product captured finer spatial heterogeneity than coarser-resolution products, particularly in fragmented forests and along forest edges. In 2024, mean forest litterfall production across China was 397 g m-2, with higher values generally found at lower latitudes and in evergreen forests. From 2000 to 2024, litterfall production increased across approximately 75 % of China's forest areas, leading to an overall increase in national total litterfall input. Precipitation showed the strongest association with interannual litterfall variations, particularly in northeastern China, whereas temperature-related associations were more pronounced in southern and northwestern China. These results provide new spatial evidence for understanding forest litterfall dynamics and support improved representation of litter inputs in carbon and nutrient cycling assessments.
Litter decomposition is a fundamental ecological process contributing to nutrient return, the formation of soil organic matter, and soil respiration. Reliable estimates of litter production are therefore essential for understanding forest and soil carbon dynamics. The dataset presented in this study thus has clear scientific significance and considerable long-term value.
Two recent studies have examined litter-derived carbon fluxes in forest ecosystems (Wang et al., 2026, https://doi.org/10.1016/j.rse.2026.115373; Tan et al., 2026, https://doi.org/10.1111/gcb.71011). Wang et al. (2026) is particularly relevant, as it estimated global forest litter production from 2000 to 2022 at annual and 500-m resolutions. Its use and interpretation of remote-sensing indices appear to provide clearer ecological context than those in the present manuscript and may offer a useful reference for improving the interpretation and application of remote-sensing predictors here.
I appreciate the authors’ efforts in compiling and developing this dataset. I offer the following comments and suggestions in the hope of further improving the manuscript.
Indeed, summing the numbers of training instances across all ten folds would yield 384 × 9 = 3456. However, these represent repeated uses of the same 384 observations rather than 3,456 independent samples. Cross-validation resampling should not increase the effective sample size; otherwise, increasing the number of folds would artificially inflate the reported n. I therefore suggest that the authors clarify how the sample sizes in Figure 2 were calculated and clearly distinguish the number of unique observations from the cumulative number of training instances across cross-validation folds.