Literature DB >> 26406633

Monte Carlo simulation of spectral reflectance and BRDF of the bubble layer in the upper ocean.

Lanxin Ma, Fuqiang Wang, Chengan Wang, Chengchao Wang, Jianyu Tan.   

Abstract

The presence of bubbles can significantly change the radiative properties of seawater and these changes will affect remote sensing and underwater target detection. In this work, the spectral reflectance and bidirectional reflectance characteristics of the bubble layer in the upper ocean are investigated using the Monte Carlo method. The Hall-Novarini (HN) bubble population model, which considers the effect of wind speed and depth on the bubble size distribution, is used. The scattering coefficients and the scattering phase functions of bubbles in seawater are calculated using Mie theory, and the inherent optical properties of seawater for wavelengths between 300 nm and 800 nm are related to chlorophyll concentration (Chl). The effects of bubble coating, Chl, and bubble number density on the spectral reflectance of the bubble layer are studied. The bidirectional reflectance distribution function (BRDF) of the bubble layer for both normal and oblique incidence is also investigated. The results show that bubble populations in clear waters under high wind speed conditions significantly influence the reflection characteristics of the bubble layer. Furthermore, the contribution of bubble populations to the reflection characteristics is mainly due to the strong backscattering of bubbles that are coated with an organic film.

Entities:  

Year:  2015        PMID: 26406633     DOI: 10.1364/OE.23.024274

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  3 in total

1.  Underwater Object Segmentation Based on Optical Features.

Authors:  Zhe Chen; Zhen Zhang; Yang Bu; Fengzhao Dai; Tanghuai Fan; Huibin Wang
Journal:  Sensors (Basel)       Date:  2018-01-12       Impact factor: 3.576

2.  Evaluation and Design of Colored Silicon Nanoparticle Systems Using a Bidirectional Deep Neural Network.

Authors:  Yan Zhou; Lechuan Hu; Chengchao Wang; Lanxin Ma
Journal:  Nanomaterials (Basel)       Date:  2022-08-07       Impact factor: 5.719

3.  Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network.

Authors:  Lanxin Ma; Kaixiang Hu; Chengchao Wang; Jia-Yue Yang; Linhua Liu
Journal:  Nanomaterials (Basel)       Date:  2021-12-08       Impact factor: 5.076

  3 in total

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