Abstract
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.
Abstract (translated)
我们提出了VibrantSR（Vibrant超分辨率），这是一个用于从10米Sentinel-2影像估算0.5米冠层高度模型（CHM）的生成性超分辨率框架。与基于航空影像的方法受限于不频繁且不规律的采集时间表不同，VibrantSR利用了全球可用的Sentinel-2季节合成数据，从而能够在季度至年度的时间尺度上进行一致监测。在使用美国环保署（EPA）Level 3生态区域中22个空间独立验证集的情况下，VibrantSR对于冠层高度≥2米的情况实现了4.39米的平均绝对误差，优于基于卫星的Meta（4.83米）、LANDFIRE（5.96米）和ETH（7.05米）基准。虽然基于航空影像的VibrantVS仍保持着较低误差的优势（2.71米MAE），但VibrantSR能够实现在大陆尺度上进行森林监测和碳排放核算，而不依赖于成本高昂且时间间隔长的航空采集。
URL
https://arxiv.org/abs/2601.09866
