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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Remote Visible/Shortwave Infrared (VSWIR) imaging spectroscopy is a powerful tool for measuring the composition of Earth’s surface over wide areas. This compositional information is captured by the spectral surface reflectance, where distinct shapes and absorption features indicate the chemical, bio- and geophysical properties of the materials in the scene. Estimating this surface reflectance requires removing the influence of atmospheric distortions caused by water vapor and particles. Traditionally reflectance is estimated by considering one location at a time, disentangling atmospheric and surface effects independently at all locations in a scene. However, this approach does not take advantage of spatial correlations between contiguous pixels. We propose an extension to a common Bayesian approach, Optimal Estimation, by introducing atmospheric correlations into the multivariate Gaussian prior. We show how this approach can be implemented as a small change to the traditional estimation procedure, thus limiting the additional computational burden. We demonstrate a simple version of the technique using simulations and multiple airborne radiance data sets. Our results show that the predicted atmospheric fields are smoother and more realistic than independent inversions given the assumption of spatial correlation and may reduce bias in the surface reflectance retrievals compared to post-process smoothing.

Details

Title
Spatial Surface Reflectance Retrievals for Visible/Shortwave Infrared Remote Sensing via Gaussian Process Priors
Author
Zilber, Daniel 1   VIAFID ORCID Logo  ; Thompson, David R 2   VIAFID ORCID Logo  ; Katzfuss, Matthias 1 ; Natraj, Vijay 2 ; Hobbs, Jonathan 2   VIAFID ORCID Logo  ; Braverman, Amy 2 

 Department of Statistics, Texas A&M University, College Station, TX 77843, USA; [email protected] 
 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA; [email protected] (D.R.T.); [email protected] (V.N.); [email protected] (J.H.); [email protected] (A.B.) 
First page
2183
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2663143782
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.