Sub-pixel mapping of disturbance and tree mortality dynamics from Sentinel-2 time series around the globe

Elevated forest disturbances and excess tree mortality are increasingly reported worldwide. Yet existing assessments are either based on patchy terrestrial observations or on large-scale satellite products, which are limited in resolution to pixel-level, binary tree loss detection. This leaves a blind spot on fine-scale disturbances where only a few trees are declining in an otherwise intact canopy. Here, we present a methodology for annually mapping sub-pixel fractional cover of standing deadwood and trees from rolling four-year windows of Sentinel-2 time series. Fractional cover is the proportion of each pixel covered by dead or live tree crowns. To obtain globally distributed sub-pixel reference labels, we leveraged the crowd-sourced archive deadtrees.earth of centimeter-scale drone orthophotos with two globally calibrated semantic segmentation models to derive tree and standing deadwood masks, yielding 6.2 million labeled Sentinel-2 pixels. Spatial block cross-validation shows high performance for tree cover, Pearson’s r = 0.58–0.64, across biomes, and moderate performance for standing deadwood cover, Pearson’s r = 0.30–0.56. Viewed as a binary classification task, the model achieves a mean precision of 91% and recall of 90% for tree cover, and a precision of 54% and recall of 82% for standing deadwood. Validation against independently obtained and ground-validated data in all major biomes reproduced known disturbance patterns and timing at unprecedented spatial detail compared to state-of-the-art products. Our method provides the critically needed missing link between fine-scale round observations and low-resolution remote sensing products, allowing more realistic estimates of global trends in forest disturbance and tree mortality.