Fine-scale forest monitoring is essential for understanding canopy structure and its dynamics, which are key indicators of carbon stocks, biodiversity, and forest health. Deep learning is particularly effective for this task, as it integrates spectral, temporal, and spatial signals that jointly reflect the canopy structure. To address this need, we introduce THREASURE-Net, a novel end-to-end framework for Tree Height Regression And Super-Resolution. The model is trained on Sentinel-2 time series using reference height metrics derived from LiDAR HD data at multiple spatial resolutions over Metropolitan France to produce annual height maps. We evaluate three model variants, producing tree-height predictions at 2.5 m, 5 m, and 10 m resolution. THREASURE-Net does not rely on any pretrained model nor on reference very high resolution optical imagery to train its super-resolution module; instead, it learns solely from LiDAR-derived height information. Our approach outperforms existing state-of-the-art methods based on Sentinel data and is competitive with methods based on very high resolution imagery. It can be deployed to generate high-precision annual canopy-height maps, achieving mean absolute errors of 2.62 m, 2.72 m, and 2.88 m at 2.5 m, 5 m, and 10 m resolution, respectively. These results highlight the potential of THREASURE-Net for scalable and cost-effective structural monitoring of temperate forests using only freely available satellite data. The source code for THREASURE-Net is available at: https://github.com/Global-Earth-Observation/threasure-net.

翻译：精细尺度的森林监测对于理解冠层结构及其动态至关重要，这些动态是碳储量、生物多样性和森林健康的关键指标。深度学习在此任务中尤为有效，因其能整合共同反映冠层结构的光谱、时序与空间信号。为满足这一需求，我们提出了THREASURE-Net——一种新颖的端到端树高回归与超分辨率框架。该模型基于法国本土多空间分辨率的LiDAR HD数据提取的参考高度指标，利用Sentinel-2时间序列进行训练，以生成年度高度图。我们评估了三种模型变体，分别生成2.5米、5米和10米分辨率的树高预测。THREASURE-Net的超分辨率模块不依赖任何预训练模型或参考的极高分辨率光学影像，仅通过LiDAR衍生的高度信息进行学习。我们的方法在基于Sentinel数据的方法中优于现有先进技术，并与基于极高分辨率影像的方法具有竞争力。该方法可部署用于生成高精度年度冠层高度图，在2.5米、5米和10米分辨率下分别实现2.62米、2.72米和2.88米的平均绝对误差。这些结果凸显了THREASURE-Net仅利用免费卫星数据即可实现温带森林可扩展、高性价比结构监测的潜力。THREASURE-Net源代码发布于：https://github.com/Global-Earth-Observation/threasure-net。