Articles | Volume 14, issue 6
Research article
04 Jun 2021
Research article |  | 04 Jun 2021

Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, Peng-Wang Zhai, Vanderlei Martins, Sharon Burton, Brian Cairns, Richard Ferrare, Joel Gales, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Brent McBride, Anin Puthukkudy, P. Jeremy Werdell, and Xiaoguang Xu

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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals
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The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color
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Analysis of simultaneous aerosol and ocean glint retrieval using multi-angle observations
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Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Cited articles

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Short summary
Multi-angle polarimetric measurements can retrieve accurate aerosol properties over complex atmosphere and ocean systems; however, most retrieval algorithms require high computational costs. We propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems and then conduct simultaneous aerosol and ocean color retrievals on AirHARP measurements. The computational acceleration is 103 times with CPU or 104 times with GPU.