Thompson David R, Bohn Niklas, Brodrick Philip G, Carmon Nimrod, Eastwood Michael L, Eckert Regina, Fichot Cédric G, Harringmeyer Joshua P, Nguyen Hai M, Simard Marc, Thorpe Andrew K
Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Potsdam Germany.
J Geophys Res Biogeosci. 2022 Jun;127(6):e2021JG006711. doi: 10.1029/2021JG006711. Epub 2022 Jun 27.
Future global Visible Shortwave Infrared Imaging Spectrometers, such as the Surface Biology and Geology (SBG) mission, will regularly cover the Earth's entire terrestrial land area. These missions need high fidelity atmospheric correction to produce consistent maps of terrestrial and aquatic ecosystem traits. However, estimation of surface reflectance and atmospheric state is computationally challenging, and the terabyte data volumes of global missions will exceed available processing capacity. This article describes how missions can overcome this bottleneck using the spatial continuity of atmospheric fields. Contemporary imaging spectrometers oversample atmospheric spatial variability, so it is not necessary to invert every pixel. Spatially sparse solutions can train local linear emulators that provide fast, exact inversions in their vicinity. We find that estimating the atmosphere at 200 m scales can outperform traditional atmospheric correction, improving speed by one to two orders of magnitude with no measurable penalty to accuracy. We validate performance with an airborne field campaign, showing reflectance accuracies with RMSE of 1.1% or better compared to ground measurements of diverse targets. These errors are statistically consistent with retrieval uncertainty budgets. Local emulators can close the efficiency gap and make rigorous model inversion algorithms feasible for global missions such as SBG.
未来的全球可见短波红外成像光谱仪,如“地表生物学与地质学”(SBG)任务,将定期覆盖地球的整个陆地面积。这些任务需要高精度的大气校正,以生成陆地和水生生态系统特征的一致地图。然而,地表反射率和大气状态的估计在计算上具有挑战性,全球任务的数TB数据量将超过可用的处理能力。本文描述了任务如何利用大气场的空间连续性来克服这一瓶颈。当代成像光谱仪对大气空间变异性进行过采样,因此无需对每个像素进行反演。空间稀疏解可以训练局部线性模拟器,在其附近提供快速、精确的反演。我们发现,在200米尺度上估计大气可以优于传统的大气校正,速度提高一到两个数量级,而对精度没有可测量的损失。我们通过一次机载实地测量验证了性能,与不同目标的地面测量相比,显示出均方根误差(RMSE)为1.1%或更好的反射率精度。这些误差在统计上与反演不确定性预算一致。局部模拟器可以缩小效率差距,使严格的模型反演算法对于像SBG这样的全球任务变得可行。