We present VibrantSR (Vibrant Super-Resolution), a generative super-resolution framework for estimating 0.5 meter canopy height models (CHMs) from 10 meter Sentinel-2 imagery. Unlike approaches based on aerial imagery that are constrained by infrequent and irregular acquisition schedules, VibrantSR leverages globally available Sentinel-2 seasonal composites, enabling consistent monitoring at a seasonal-to-annual cadence. Evaluated across 22 EPA Level 3 eco-regions in the western United States using spatially disjoint validation splits, VibrantSR achieves a Mean Absolute Error of 4.39 meters for canopy heights >= 2 m, outperforming Meta (4.83 m), LANDFIRE (5.96 m), and ETH (7.05 m) satellite-based benchmarks. While aerial-based VibrantVS (2.71 m MAE) retains an accuracy advantage, VibrantSR enables operational forest monitoring and carbon accounting at continental scales without reliance on costly and temporally infrequent aerial acquisitions.
Cite
@article{arxiv.2601.09866,
title = {VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching},
author = {Kiarie Ndegwa and Andreas Gros and Tony Chang and David Diaz and Vincent A. Landau and Nathan E. Rutenbeck and Luke J. Zachmann and Guy Bayes and Scott Conway},
journal= {arXiv preprint arXiv:2601.09866},
year = {2026}
}