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Lidar Remote Sensing of Seawater Optical Properties: Experiment and Monte Carlo Simulation.

Authors :
Liu, Dong
Chen, Peng
Che, Haochi
Liu, Zhipeng
Liu, Qun
Song, Qingjun
Chen, Sijie
Xu, Peituo
Zhou, Yudi
Chen, Weibiao
Han, Bing
Zhu, Xiaolei
He, Yan
Mao, Zhihua
Le, Chengfeng
Source :
IEEE Transactions on Geoscience & Remote Sensing. Nov2019, Vol. 57 Issue 11, p9489-9498. 10p.
Publication Year :
2019

Abstract

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient $\alpha $. The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured $\alpha $ and MC-simulated $\alpha $ at different stations ($r = 0.95$) and at various FOVs ($r = 0.96$). The results indicate the reliability of the developed lidar system. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
57
Issue :
11
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
140084479
Full Text :
https://doi.org/10.1109/TGRS.2019.2926891