SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping

TL;DR

SERA-H is an innovative deep learning model that leverages super-resolution and temporal attention to produce high-resolution canopy height maps from freely available satellite data, surpassing traditional methods in accuracy.

Contribution

The paper introduces SERA-H, a novel end-to-end model combining super-resolution and temporal encoding, enabling high-resolution forest canopy mapping from low-resolution satellite data.

Findings

Achieves 2.6 m MAE and 0.82 R2 on benchmark datasets.

Outperforms standard Sentinel-1/2 baselines.

Comparable or better than commercial high-resolution imagery methods.

Abstract

High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these approaches often face a trade-off between data accessibility and spatial resolution. To overcome these limitations, we present SERA-H, an end-to-end model combining a super-resolution module (EDSR) and temporal attention encoding (UTAE). Trained under the supervision of high-density LiDAR-derived Canopy Height Models (CHM), our model generates 2.5 m resolution height maps from freely available Sentinel-1 and Sentinel-2 (10 m) time series data. Evaluated on an open-source benchmark dataset in France, SERA-H, with a MAE of 2.6 m and R2 of 0.82, not only outperforms standard Sentinel- 1/2 baselines but also achieves performance comparable to or better than methods relying on commercial very high-resolution imagery (SPOT-6/7, PlanetScope, Maxar). These results demonstrate that combining high-resolution supervision with the spatiotemporal information embedded in time series enables the reconstruction of details beyond the input sensors' native resolution. SERA-H opens the possibility of freely mapping forests with high revisit frequency, achieving accuracy comparable to that of costly commercial imagery.