Abstract Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.

中文翻译：

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融合模拟 GEDI、ICESat-2 和 NISAR 数据用于区域地上生物量绘图

摘要 森林地上生物量 (AGB) 的准确绘图对于更好地理解森林在全球碳循环中的作用至关重要。NASA 当前的 GEDI 和 ICESat-2 任务以及即将到来的 NISAR 任务将收集对 AGB 具有不同覆盖范围和敏感性的协同数据。在这项研究中，我们提出了一种多传感器数据融合方法，利用每个任务的强度来制作墙到墙 AGB 地图，该地图比单独使用任何一个传感器所能实现的更准确和空间全面。具体来说，我们使用稀疏的模拟星载激光雷达 AGB 估计值校准区域 L 波段雷达 AGB 模型。我们使用 GEDI 模拟评估我们的数据融合框架，来自机载激光扫描 (ALS) 和 UAVSAR 数据的 ICESat-2 和 NISAR 数据在美国加利福尼亚州索诺玛县的温带高 AGB 森林和复杂地形上获得。对于 ICESat-2 和 GEDI 任务，我们模拟了两年的数据覆盖范围，并使用真实的 AGB 模型估计了足迹级别的 AGB。当稀疏模拟 GEDI 和 ICEsat-2 AGB 估计值的不同组合用于校准我们的区域 L 波段 AGB 模型时，我们比较了融合框架的性能。此外，我们使用 (a) 1 公顷方形网格单元和 (b) 类似大小的不规则形状物体在索诺玛测试我们的框架。我们证明了使用我们的融合框架比单独使用 GEDI 或 ICESat-2 任务数据更准确地估计了索诺玛的估计平均 AGB，以规则网格或不规则段作为映射单元。这项研究强调了通过数据融合将新的和即将到来的主动遥感数据流融合到改进的 AGB 制图的方法论机会。