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.

翻译：高分辨率冠层高度制图对于森林管理与生物多样性监测至关重要。尽管近期研究已催生出利用卫星影像预测高度图的深度学习方法，但这些方法常在数据可获取性与空间分辨率之间面临权衡。为克服这些限制，我们提出了SERA-H，一种结合超分辨率模块（EDSR）与时序注意力编码（UTAE）的端到端模型。在高密度激光雷达数据（ALS）的监督下训练，我们的模型能够从免费获取的Sentinel-1与Sentinel-2（10米分辨率）时序数据中生成2.5米分辨率的高度图。在法国的一个开源基准数据集上评估，SERA-H以2.6米的平均绝对误差和0.82的决定系数，不仅超越了标准的Sentinel-1/2基线方法，更取得了与依赖商业甚高分辨率影像（SPOT-6/7、PlanetScope、Maxar）的方法相当或更优的性能。这些结果表明，将高分辨率监督数据与时序数据中嵌入的时空信息相结合，能够重建超出输入传感器原生分辨率的细节。SERA-H为以高重访频率、免费绘制森林图提供了可能，其精度可与昂贵的商业影像相媲美。