气候变化领域集成服务门户(CCPortal): Uncertainty quantification for a global imaging spectroscopy surface composition investigation

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DOI	10.1016/j.rse.2020.112038
	Uncertainty quantification for a global imaging spectroscopy surface composition investigation
	Carmon N.; Thompson D.R.; Bohn N.; Susiluoto J.; Turmon M.; Brodrick P.G.; Connelly D.S.; Braverman A.; Cawse-Nicholson K.; Green R.O.; Gunson M.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	Airborne and orbital imaging spectroscopy can facilitate the quantification of chemical and physical attributes of surface materials through analysis of spectral signatures. Prior to analysis, estimates of surface reflectance must be inferred from radiance measurements in a process known as atmospheric correction, which compensates for the distortion of the electromagnetic signal by the atmosphere. Inaccuracies in the correction process can alter characteristic spectral signatures, leading to subsequent mischaracterization of surface properties. Global observations pose new challenges for mapping surface composition, as varied atmospheric conditions and surface biomes challenge traditional atmospheric correction methods. Recent work adopted an optimal estimation (OE) approach for retrieving surface reflectance from observed radiance measurements, providing the reflectance estimates with a posterior probability. This work incorporates these input probabilities to improve the accuracy of surface feature measurements. We demonstrate this using a generic feature-fitting method that is applicable to a wide range of Earth surface studies including geology, ecosystem studies, hydrology and urban studies. Specifically, we use a probabilistic framework based on generalized Tikhonov-regularized least squares, a rigorous formulation for appropriate weighting of features by their observation uncertainty and leveraging of prior knowledge of material abundance for improving estimation accuracy. We demonstrate the validity of this procedure and quantify the increase in model performance by simulating expected accuracies in the reflectance estimation. To evaluate global uncertainties in mineral estimation, we simulate observations representative of the expected global range of atmospheric water vapor and aerosol levels, and characterize the sensitivity of our procedure to those quantities. © 2020 The Authors
英文关键词	Global imaging spectroscopy; Minerals; Optimization; Reflectance; Remote sensing; Surface mapping; Uncertainty propagation; Uncertainty quantification
语种	英语
scopus关键词	Chemical analysis; Knowledge management; Orbits; Reflection; Uncertainty analysis; Atmospheric corrections; Atmospheric water vapor; Electromagnetic signals; Observation uncertainties; Probabilistic framework; Reflectance estimations; Regularized least squares; Uncertainty quantifications; Atmospheric chemistry; accuracy assessment; airborne survey; atmospheric correction; electromagnetic field; global change; imaging method; model validation; quantitative analysis; spectral reflectance; spectroscopy; uncertainty analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179097
作者单位	Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; GFZ German Research Centre for Geosciences, Remote Sensing and Geoinformatics, Potsdam, Germany; Cornell University, Ithaca, NY, United States
推荐引用方式 GB/T 7714	Carmon N.,Thompson D.R.,Bohn N.,et al. Uncertainty quantification for a global imaging spectroscopy surface composition investigation[J],2020,251.
APA	Carmon N..,Thompson D.R..,Bohn N..,Susiluoto J..,Turmon M..,...&Gunson M..(2020).Uncertainty quantification for a global imaging spectroscopy surface composition investigation.Remote Sensing of Environment,251.
MLA	Carmon N.,et al."Uncertainty quantification for a global imaging spectroscopy surface composition investigation".Remote Sensing of Environment 251(2020).