Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties

Yudi Zhou; Yang Chen; Hongkai Zhao; Cédric Jamet; Davide Dionisi; Malik Chami; Paolo Di Girolamo; James H. Churnside; Aleksey Malinka; Huade Zhao; Dajun Qiu; Tingwei Cui; Qun Liu; Yatong Chen; Sornsiri Phongphattarawat; Nanchao Wang; Sijie Chen; Peng Chen; Ziwei Yao; Chengfeng Le; Yuting Tao; Peituo Xu; Xiaobin Wang; Binyu Wang; Feitong Chen; Chuang Ye; Kai Zhang; Chong Liu; Dong Liu

doi:10.1038/s41377-022-00951-0

Go to Nature Website

Your Location：

Home >

Browse articles >

Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties

Original Articles

- Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties
- Light: Science & Applications Vol. 11, Issue 10, Pages: 2345-2357(2022)
- Affiliations：
  
  1.Ningbo Research Institute, State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
  2.Intelligent Optics & Photonics Research Center, Jiaxing Key Laboratory of Photonic Sensing & Intelligent Imaging, Jiaxing Research Institute, Zhejiang University, Jiaxing, 314000, China
  3.Univ. Littoral Côte d'Opale, CNRS, Univ. Lille, IRD, UMR 8187 - LOG - Laboratoire d'Océanologie et de Géosciences, F-62930, Wimereux, France
  4.Institute of Marine Sciences (ISMAR), Italian National Research Council (CNR), Rome - Tor Vergata, 00133, Italy
  5.Sorbonne Université, CNRS, LATMOS, 96 Boulevard de l'Observatoire, 06304, Nice Cedex, France
  6.Scuola di Ingegneria, Università della Basilicata, Viale Ateneo Lucano 10, I-85100, Potenza, Italy
  7.Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder and NOAA Chemical Sciences Laboratory, 325 Broadway, Boulder, CO, 80305, USA
  8.Institute of Physics, National Academy of Sciences of Belarus, Pr. Nezavisimosti 68-2, Minsk, 220072, Belarus
  9.Key Laboratory for Ecological Environment in Coastal Areas (State Oceanic Administration), National Marine Environmental Monitoring Center, Dalian, 116023, China
  10.CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
  11.School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai, 519000, China
  12.Faculty of Technology and Environment, Prince of Songkla University, Phuket, 83120, Thailand
  13.Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China
  14.Ocean College, Zhejiang University, Zhoushan, 316021, China
  15.Donghai Laboratory, Zhoushan, 316021, China
- Author bio：
  
  Dong Liu (liudongopt@zju.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-00951-0
  CLC：
- Published：31 October 2022，
  
  Published Online：02 September 2022，
  
  Received：16 February 2022，
  
  Revised：18 July 2022，
  
  Accepted：07 August 2022
- Accepted：
Scan QR Code
Zhou, Y. D. et al. Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties. Light: Science & Applications, 11, 2345-2357 (2022).
DOI：

Zhou, Y. D. et al. Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties. Light: Science & Applications, 11, 2345-2357 (2022). DOI： 10.1038/s41377-022-00951-0.

摘要

Abstract

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However

accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of

to the best of our knowledge

the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient

and the particulate backscattering coefficient

at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea

which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for

and

respectively

corresponding to an improvement of 2.7–13.5 and 4.9–44.1 times

respectively

with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

关键词

Keywords

references

M. J. Behrenfeld , 等 . Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar . Nat. Geosci. , 2017 . 10 118 -122 . DOI:10.1038/ngeo2861http://doi.org/10.1038/ngeo2861 .

M. J. Behrenfeld , 等 . Global satellite-observed daily vertical migrations of ocean animals . Nature , 2019 . 576 257 -261 . DOI:10.1038/s41586-019-1796-9http://doi.org/10.1038/s41586-019-1796-9 .

C. A. Hostetler , 等 . Spaceborne lidar in the study of marine systems . Annu. Rev. Mar. Sci. , 2018 . 10 121 -147 . DOI:10.1146/annurev-marine-121916-063335http://doi.org/10.1146/annurev-marine-121916-063335 .

M. J. Behrenfeld , 等 . Space‐based lidar measurements of global ocean carbon stocks . Geophys. Res. Lett. , 2013 . 40 4355 -4360 . DOI:10.1002/grl.50816http://doi.org/10.1002/grl.50816 .

J. H. Churnside , R. D. Marchbanks . Subsurface plankton layers in the Arctic Ocean . Geophys. Res. Lett. , 2015 . 42 4896 -4902 . DOI:10.1002/2015GL064503http://doi.org/10.1002/2015GL064503 .

P. Chen , 等 . Vertical distribution of subsurface phytoplankton layer in South China Sea using airborne lidar . Remote Sens. Environ. , 2021 . 263 112567 DOI:10.1016/j.rse.2021.112567http://doi.org/10.1016/j.rse.2021.112567 .

X. M. Lu , 等 . Antarctic spring ice-edge blooms observed from space by ICESat-2 . Remote Sens. Environ. , 2020 . 245 111827 DOI:10.1016/j.rse.2020.111827http://doi.org/10.1016/j.rse.2020.111827 .

A. Sengupta , F. Carrara , R. Stocker . Phytoplankton can actively diversify their migration strategy in response to turbulent cues . Nature , 2017 . 543 555 -558 . DOI:10.1038/nature21415http://doi.org/10.1038/nature21415 .

M. J. Behrenfeld , 等 . Revaluating ocean warming impacts on global phytoplankton . Nat. Clim. Change , 2016 . 6 323 -330 . DOI:10.1038/nclimate2838http://doi.org/10.1038/nclimate2838 .

M. J. Behrenfeld , 等 . Climate-driven trends in contemporary ocean productivity . Nature , 2006 . 444 752 -755 . DOI:10.1038/nature05317http://doi.org/10.1038/nature05317 .

A. Mahadevan , 等 . Eddy-driven stratification initiates North Atlantic spring phytoplankton blooms . Science , 2012 . 337 54 -58 . DOI:10.1126/science.1218740http://doi.org/10.1126/science.1218740 .

K. R. Arrigo , 等 . Massive phytoplankton blooms under Arctic Sea ice . Science , 2012 . 336 1408 -1408 . DOI:10.1126/science.1215065http://doi.org/10.1126/science.1215065 .

C. Jamet , 等 . Going beyond standard ocean color observations: lidar and polarimetry . Front. Mar. Sci. , 2019 . 6 251 DOI:10.3389/fmars.2019.00251http://doi.org/10.3389/fmars.2019.00251 .

D. Liu , 等 . Phase function effects on the retrieval of oceanic high-spectral-resolution lidar . Opt. Express , 2019 . 27 DOI:10.1364/OE.27.00A654http://doi.org/10.1364/OE.27.00A654 .

B. L. Collister , 等 . Polarized lidar and ocean particles: insights from a mesoscale coccolithophore bloom . Appl. Opt. , 2020 . 59 4650 -4662 . DOI:10.1364/AO.389845http://doi.org/10.1364/AO.389845 .

D. Dionisi , 等 . Seasonal distributions of ocean particulate optical properties from spaceborne lidar measurements in Mediterranean and Black sea . Remote Sens. Environ. , 2020 . 247 111889 DOI:10.1016/j.rse.2020.111889http://doi.org/10.1016/j.rse.2020.111889 .

J. H. Churnside , 等 . Ocean backscatter profiling using high-spectral-resolution lidar and a perturbation retrieval . Remote Sens. , 2018 . 10 2003 DOI:10.3390/rs10122003http://doi.org/10.3390/rs10122003 .

F. G. Fernald . Analysis of atmospheric lidar observations: some comments . Appl. Opt. , 1984 . 23 652 -653 . DOI:10.1364/AO.23.000652http://doi.org/10.1364/AO.23.000652 .

S. T.High spectral resolution lidar to measure optical scattering properties of atmospheric aerosols. Shipley , 等 . 1: theory and instrumentation . Appl. Opt. , 1983 . 22 3716 -3724 . DOI:10.1364/AO.22.003716http://doi.org/10.1364/AO.22.003716 .

J. W. Hair , 等 . Airborne High Spectral Resolution Lidar for profiling aerosol optical properties . Appl. Opt. , 2008 . 47 6734 -6752 . DOI:10.1364/AO.47.006734http://doi.org/10.1364/AO.47.006734 .

N. C. Wang , 等 . Dual-field-of-view high-spectral-resolution lidar: simultaneous profiling of aerosol and water cloud to study aerosol-cloud interaction . Proc. Natl Acad. Sci. USA , 2022 . 119 DOI:10.1073/pnas.2110756119http://doi.org/10.1073/pnas.2110756119 .

J. Ke , 等 . Development of China's first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration . PhotoniX , 2022 . 3 17 DOI:10.1186/s43074-022-00063-3http://doi.org/10.1186/s43074-022-00063-3 .

J. A. Schulien , 等 . Vertically-resolved phytoplankton carbon and net primary production from a high spectral resolution lidar . Opt. Express , 2017 . 25 13577 -13587 . DOI:10.1364/OE.25.013577http://doi.org/10.1364/OE.25.013577 .

J. A. Schulien , 等 . Shifts in phytoplankton community structure across an anticyclonic eddy revealed from high spectral resolution lidar scattering measurements . Front. Mar. Sci. , 2020 . 7 493 DOI:10.3389/fmars.2020.00493http://doi.org/10.3389/fmars.2020.00493 .

J. Hair , 等 . Combined atmospheric and ocean profiling from an airborne high spectral resolution lidar . EPJ Web Conf. , 2016 . 119 22001 DOI:10.1051/epjconf/201611922001http://doi.org/10.1051/epjconf/201611922001 .

Y. D. Zhou , 等 . Retrieving the seawater volume scattering function at the 180° scattering angle with a high-spectral-resolution lidar . Opt. Express , 2017 . 25 11813 -11826 . DOI:10.1364/OE.25.011813http://doi.org/10.1364/OE.25.011813 .

Y. D. Zhou , 等 . Multiple scattering effects on the return spectrum of oceanic high-spectral-resolution lidar . Opt. Express , 2019 . 27 30204 -30216 . DOI:10.1364/OE.27.030204http://doi.org/10.1364/OE.27.030204 .

D. Liu , 等 . Lidar remote sensing of seawater optical properties: experiment and Monte Carlo simulation . IEEE Trans. Geosci. Remote Sens. , 2019 . 57 9489 -9498 . DOI:10.1109/TGRS.2019.2926891http://doi.org/10.1109/TGRS.2019.2926891 .

H. R. Gordon . Interpretation of airborne oceanic lidar: effects of multiple scattering . Appl. Opt. , 1982 . 21 2996 -3001 . DOI:10.1364/AO.21.002996http://doi.org/10.1364/AO.21.002996 .

R. E. Walker , J. W. Mclean . Lidar equations for turbid media with pulse stretching . Appl. Opt. , 1999 . 38 2384 -2397 . DOI:10.1364/AO.38.002384http://doi.org/10.1364/AO.38.002384 .

A. V. Malinka , E. P. Zege . Analytical modeling of Raman lidar return, including multiple scattering . Appl. Opt. , 2003 . 42 1075 -1081 . DOI:10.1364/AO.42.001075http://doi.org/10.1364/AO.42.001075 .

Born, M. & Wolf, E. Principles of optics: electromagnetic theory of propagation, interference and diffraction of light 7th edn. (Cambridge: Cambridge University Press, 2013).

Bian, C. W. et al. Distributions of suspended sediment concentration in the Yellow Sea and the East China Sea based on field surveys during the four seasons of 2011. J. Mar. Syst. 121-122, 24–35 (2013).

F. X. Zhou , 等 . Absorption properties of chromophoric dissolved organic matter (CDOM) in the East China Sea and the waters off eastern Taiwan . Continental Shelf Res. , 2018 . 159 12 -23 . DOI:10.1016/j.csr.2018.03.005http://doi.org/10.1016/j.csr.2018.03.005 .

J. P. Liu , 等 . Flux and fate of Yangtze river sediment delivered to the East China Sea . Geomorphology , 2007 . 85 208 -224 . DOI:10.1016/j.geomorph.2006.03.023http://doi.org/10.1016/j.geomorph.2006.03.023 .

X. G. Xing , 等 . Temporal and vertical variations of particulate and dissolved optical properties in the South China Sea . J. Geophys. Res. Oceans , 2019 . 124 3779 -3795 . DOI:10.1029/2018JC014880http://doi.org/10.1029/2018JC014880 .

J. H. Churnside , J. M. Sullivan , M. S. Twardowski . Lidar extinction-to-backscatter ratio of the ocean . Opt. Express , 2014 . 22 18698 -18706 . DOI:10.1364/OE.22.018698http://doi.org/10.1364/OE.22.018698 .

Mobley, C. D. Light and water: radiative transfer in natural waters (San Diego: Academic Press, 1994).

J. H. Churnside , L. A. Ostrovsky . Lidar observation of a strongly nonlinear internal wave train in the Gulf of Alaska . Int. J. Remote Sens. , 2005 . 26 167 -177 . DOI:10.1080/01431160410001735076http://doi.org/10.1080/01431160410001735076 .

K. M. Bisson , 等 . Particulate backscattering in the global ocean: a comparison of independent assessments . Geophys. Res. Lett. , 2021 . 48 .

H. Y. Sun , 等 . Detection of surface defects and subsurface defects of polished optics with multisensor image fusion . PhotoniX , 2022 . 3 6 DOI:10.1186/s43074-022-00051-7http://doi.org/10.1186/s43074-022-00051-7 .

B. L. Collister , 等 . Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar . Remote Sens. Environ. , 2018 . 215 85 -96 . DOI:10.1016/j.rse.2018.05.032http://doi.org/10.1016/j.rse.2018.05.032 .

W. M. Durham , J. O. Kessler , R. Stocker . Disruption of vertical motility by shear triggers formation of thin phytoplankton layers . Science , 2009 . 323 1067 -1070 . DOI:10.1126/science.1167334http://doi.org/10.1126/science.1167334 .

W. M. Durham , R. Stocker . Thin phytoplankton layers: characteristics, mechanisms, and consequences . Annu. Rev. Mar. Sci. , 2012 . 4 177 -207 . DOI:10.1146/annurev-marine-120710-100957http://doi.org/10.1146/annurev-marine-120710-100957 .

I. Cetinić , 等 . A simple optical index shows spatial and temporal heterogeneity in phytoplankton community composition during the 2008 North Atlantic Bloom Experiment . Biogeosciences , 2015 . 12 2179 -2194 . DOI:10.5194/bg-12-2179-2015http://doi.org/10.5194/bg-12-2179-2015 .

Q. Liu , 等 . A semianalytic Monte Carlo radiative transfer model for polarized oceanic lidar: Experiment-based comparisons and multiple scattering effects analyses . J. Quant. Spectrosc. Radiat. Transf. , 2019 . 237 106638 DOI:10.1016/j.jqsrt.2019.106638http://doi.org/10.1016/j.jqsrt.2019.106638 .

J. H. Churnside . Review of profiling oceanographic lidar . Optical Eng. , 2014 . 53 051405 DOI:10.1117/1.OE.53.5.051405http://doi.org/10.1117/1.OE.53.5.051405 .

Q. Liu , 等 . Relationship between the effective attenuation coefficient of spaceborne lidar signal and the IOPs of seawater . Opt. Express , 2018 . 26 30278 -30291 . DOI:10.1364/OE.26.030278http://doi.org/10.1364/OE.26.030278 .

Y. D. Zhou , 等 . Validation of the analytical model of oceanic lidar returns: comparisons with Monte Carlo simulations and experimental results . Remote Sens. , 2019 . 11 1870 DOI:10.3390/rs11161870http://doi.org/10.3390/rs11161870 .

Z. P. Lee , K. P. Du , R. Arnone . A model for the diffuse attenuation coefficient of downwelling irradiance . J. Geophys. Res. Oceans , 2005 . 110 .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰