Precise DEMs at high spatial resolution are indispensable for a variety of scientific studies and applications. Presently, the TanDEM-X mission possesses the capability to collect global-scale InSAR data at high spatial resolution, enabling the generation of a high-resolution global DEM (12 m). Nevertheless, directly utilizing InSAR data poses challenges in detecting sub-canopy topography within forest areas, due to the presence of volume scattering and limited penetration of X-band. Conversely, the upcoming BIOMASS mission operated in P-band will provide an exceptional opportunity for sub-canopy topography extraction, owing to its strong penetration and the capability to collect fully-polarimetric SAR data. However, it is imperative to acknowledge that BIOMASS data do have its own limitation, manifesting as lower resolution (100 m) due to limited bandwidth. To address these challenges and generate high-resolution sub-canopy topography, we propose a new method that leverages the strengths of both TanDEM-X InSAR and BIOMASS PolInSAR datasets through the wavelet transform. We evaluated the performance of our method at two test sites characterized by different forest types and terrain conditions using airborne LiDAR data. Our findings demonstrate a significant improvement in sub-canopy topography accuracy. Specifically, under the boreal coniferous forest scenario, the root mean square error (RMSE) of the resulting sub-canopy topography decreased by 44% compared to the TanDEM-X InSAR DEM. In tropical broadleaf forest scenario, the RMSE reduction reached 64% over the TanDEM-X InSAR DEM. These results indicate the potential of our approach for high-resolution sub-canopy topography mapping by combing data from these two different spaceborne SAR sensors.

高空间分辨率的精确 DEM 对于各种科学研究和应用来说是不可或缺的。目前，TanDEM-X任务具有以高空间分辨率收集全球尺度InSAR数据的能力，能够生成高分辨率的全球DEM（12 m）。然而，由于体积散射的存在和 X 波段的穿透有限，直接利用 InSAR 数据对检测森林区域内的亚冠层地形提出了挑战。相反，即将在 P 波段运行的 BIOMASS 任务，由于其强大的穿透力和收集全偏振 SAR 数据的能力，将为亚冠层地形提取提供绝佳的机会。然而，必须承认 BIOMASS 数据确实有其自身的局限性，表现为由于带宽有限而导致分辨率较低（100 m）。为了应对这些挑战并生成高分辨率的亚冠层地形，我们提出了一种新方法，通过小波变换利用 TanDEM-X InSAR 和 BIOMASS PolInSAR 数据集的优势。我们使用机载激光雷达数据在两个具有不同森林类型和地形条件的测试地点评估了我们的方法的性能。我们的研究结果表明亚冠层地形精度有了显着提高。具体而言，在北方针叶林场景下，与 TanDEM-X InSAR DEM 相比，所得亚冠层地形的均方根误差 (RMSE) 降低了 44%。在热带阔叶林场景中，RMSE 比 TanDEM-X InSAR DEM 降低了 64%。这些结果表明我们的方法通过结合这两个不同星载 SAR 传感器的数据来进行高分辨率子冠层地形测绘的潜力。