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Open AccessArticle

Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements

State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
SRON-Netherlands Institute for Space Research, Sorbonnelaan 2, NL-3584 CA Utrecht, The Netherlands
University of Chinese Academy of Sciences, Beijing 100049, China
School of Physics and Astronomy, University of Leicester, University Road, Leicester LE1 7RH, UK
NASA Langley Research Center (LaRC), Hampton, VA 23666, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2877; (registering DOI)
Received: 31 October 2019 / Revised: 26 November 2019 / Accepted: 29 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Advances of Remote Sensing Inversion)
For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties View Full-Text
Keywords: neural network; aerosols; multi-angle; polarimetry neural network; aerosols; multi-angle; polarimetry
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MDPI and ACS Style

Fan, C.; Fu, G.; Di Noia, A.; Smit, M.; H.H. Rietjens, J.; A. Ferrare, R.; Burton, S.; Li, Z.; P. Hasekamp, O. Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements. Remote Sens. 2019, 11, 2877.

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