SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping
This enables free, high-accuracy forest mapping for management and monitoring, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of high-resolution canopy height mapping by developing SERA-H, an end-to-end model that combines super-resolution and temporal attention encoding to generate 2.5 m resolution height maps from freely available Sentinel-1/2 data, achieving a MAE of 2.6 m and R² of 0.82, outperforming baselines and matching commercial imagery methods.
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 data (ALS), 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 a coefficient of determination 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.