Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data.

地上森林生物量（AGB）是生态系统结构的关键指标，在全球碳循环中起着关键作用。由于其广泛的可用性，光学遥感数据对于区域和全球范围的AGB评估至关重要，并且随着计划中的和最近启动的星载成像光谱任务（例如环境制图和分析计划（EnMAP）），了解为AGB映射添加光谱信息很重要。我们使用从美国加利福尼亚州索诺玛县获得的机载可见红外成像光谱仪（AVIRIS）图像获得的模拟EnMAP图像，结合Landsat时间序列来绘制森林AGB。实施了高斯过程回归模型，以从一两次和两次（4月和6月）的EnMAP图像估计森林AGB。基于激光雷达的参考AGB图用作训练和验证样品提取的基础数据。作为比较，我们使用了相应的Landsat最佳可用像素（BAP）复合材料以及2013年的为期一年的16天插值Landsat时间序列（TS）。EnMAP图像能够有效地绘制森林AGB的两个日期模型（RMSE = 97.5 Mg / ha）优于两个单一日期模型。所有EnMAP模型均优于相应的Landsat BAP模型，其中最好的是两日期模型（RMSE = 108.8 Mg / ha）。Landsat时间序列的附加时间维产生了最佳的基于Landsat的AGB地图（RMSE = 102.3 Mg / ha）。将两个数据集结合起来，进一步改善了AGB绘图工作，其中2天的EnMAP + 2013 Landsat TS提供了最佳的总体AGB地图（RMSE = 86.0 Mg / ha）。这项研究不仅证明了高光谱图像对于森林AGB绘图的附加价值，而且还证明了高光谱和多光谱数据源之间以及因此在光谱和时间密集信息之间可能的协同作用。可以预期，借助下一代星载高光谱传感器，密集光谱和时间数据的结合将有助于进一步改善从光学地球观测数据绘制森林AGB的全球努力。