Accurate Above-Ground Biomass (AGB) mapping at both large scale and high spatio-temporal resolution is essential for applications ranging from climate modeling to biodiversity assessment, and sustainable supply chain monitoring. At present, fine-grained AGB mapping relies on costly airborne laser scanning acquisition campaigns usually limited to regional scales. Initiatives such as the ESA CCI map attempt to generate global biomass products from diverse spaceborne sensors but at a coarser resolution. To enable global, high-resolution (HR) mapping, several works propose to regress AGB from HR satellite observations such as ESA Sentinel-1/2 images. We propose a novel way to address HR AGB estimation, by leveraging both HR satellite observations and existing low-resolution (LR) biomass products. We cast this problem as Guided Super-Resolution (GSR), aiming at upsampling LR biomass maps (sources) from $100$ to $10$ m resolution, using auxiliary HR co-registered satellite images (guides). We compare super-resolving AGB maps with and without guidance, against direct regression from satellite images, on the public BioMassters dataset. We observe that Multi-Scale Guidance (MSG) outperforms direct regression both for regression ($-780$ t/ha RMSE) and perception ($+2.0$ dB PSNR) metrics, and better captures high-biomass values, without significant computational overhead. Interestingly, unlike the RGB+Depth setting they were originally designed for, our best-performing AGB GSR approaches are those that most preserve the guide image texture. Our results make a strong case for adopting the GSR framework for accurate HR biomass mapping at scale. Our code and model weights are made publicly available (https://github.com/kaankaramanofficial/GSR4B).

翻译：在从气候建模到生物多样性评估，再到可持续供应链监测等应用中，在大尺度上同时实现高时空分辨率的地上生物量（AGB）精确制图至关重要。目前，精细尺度的AGB制图依赖于成本高昂的机载激光扫描采集活动，而这些活动通常仅限于区域尺度。诸如ESA CCI地图等计划试图从各种星载传感器生成全球生物量产品，但其分辨率较为粗糙。为了实现全球范围的高分辨率（HR）制图，已有若干研究提出从高分辨率卫星观测数据（如欧空局Sentinel-1/2图像）回归估算AGB。我们提出了一种利用高分辨率卫星观测数据和现有低分辨率（LR）生物量产品来解决高分辨率AGB估算问题的新方法。我们将此问题构建为引导式超分辨率（GSR）任务，旨在利用辅助的高分辨率、已配准的卫星图像（引导图像），将低分辨率生物量地图（源图像）从$100$米分辨率上采样至$10$米分辨率。我们在公开的BioMassters数据集上，比较了有引导和无引导条件下的生物量图超分辨率重建方法，以及直接从卫星图像进行回归的方法。我们观察到，多尺度引导（MSG）方法在回归指标（RMSE降低$780$ t/ha）和感知指标（PSNR提升$2.0$ dB）上均优于直接回归，并且能更好地捕捉高生物量值，同时没有显著的计算开销。有趣的是，与它们最初设计的RGB+深度图像应用场景不同，在我们这里表现最佳的AGB GSR方法是那些最能保留引导图像纹理的方法。我们的结果有力地证明了采用GSR框架进行大规模、精确的高分辨率生物量制图的可行性。我们的代码和模型权重已公开（https://github.com/kaankaramanofficial/GSR4B）。