{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:12:32Z","timestamp":1760227952566,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Technology Development Fund of the Jet Propulsion Laboratory, California Institute of Technology","award":["DMS\u20131521676","DMS\u20131654083","DMS\u20131953005"],"award-info":[{"award-number":["DMS\u20131521676","DMS\u20131654083","DMS\u20131953005"]}]},{"name":"ational Science Foundation (NSF)","award":["DMS\u20131521676","DMS\u20131654083","DMS\u20131953005"],"award-info":[{"award-number":["DMS\u20131521676","DMS\u20131654083","DMS\u20131953005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote Visible\/Shortwave Infrared (VSWIR) imaging spectroscopy is a powerful tool for measuring the composition of Earth\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs14092183","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:26:35Z","timestamp":1651566395000},"page":"2183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spatial Surface Reflectance Retrievals for Visible\/Shortwave Infrared Remote Sensing via Gaussian Process Priors"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4387-0271","authenticated-orcid":false,"given":"Daniel","family":"Zilber","sequence":"first","affiliation":[{"name":"Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-7550","authenticated-orcid":false,"given":"David R.","family":"Thompson","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Matthias","family":"Katzfuss","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Vijay","family":"Natraj","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1679-0898","authenticated-orcid":false,"given":"Jonathan","family":"Hobbs","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Amy","family":"Braverman","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"ref_1","unstructured":"Space Studies Board, and National Academies of Sciences, Engineering, and Medicine (2019). 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