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Remote Sensing
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Oceanographic Lidar in the Study of Marine Systems
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Special Issue "Oceanographic Lidar in the Study of Marine Systems"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 December 2023 | Viewed by 2745

Special Issue Editors

Dr. Peng Chen

E-Mail Website
Guest Editor
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Baochubei Road, Hangzhou 310012, China
Interests: ocean color; ocean lidar; ocean optics; ocean ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Cédric Jamet

E-Mail Website
Guest Editor
Univ. Littoral Cote d’Opale, Univ. Lille, CNRS, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France
Interests: active and passive remote sensing of ocean color; atmospheric correction; inversion techniques for the estimation of biogeochemical parameters
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junwu Tang
E-Mail Website
Guest Editor
Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Interests: ocean LiDAR; ocean ecology; ocean optics; ocean color remote sensing; remote sensor calibration and evaluation; atmospheric correction
Dr. Martin A. Montes

E-Mail Website
Guest Editor
1. Science Systems and Applications, Inc., (SSAI), Lanham, MD, USA
2. NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: LiDAR in aquatic environments; statistical modeling of continental aerosols; toxic phytoplankton blooms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, passive ocean color observations have expanded and refined our understanding of global plankton ecosystems. However, passive measurements are sensitive to only the very near-surface layer, providing no information on vertical structure. Specifically, the measured property is a weighted–integrated value over a relatively shallow depth, it provides no information during the night, and retrievals are compromised by clouds, absorbing aerosols, and low sun zenith angles. Additionally, the traditional way to detect the vertical structure is mainly through shipboard discrete observations or Bio-Argos, which require considerable time to cover a limited area. These limitations can be addressed by active LiDAR technology. Active LiDAR measurements could provide depth-resolved values of ocean phytoplankton properties both during daytime and nighttime. With new vertically resolved and diurnal continuous measurements, LiDAR can provide new insights into seawater bio-optical vertical structure, which will enhance our understanding of biogeochemical processes.

This Special Issue aims to bring some of the leading scholars in the fields of LiDAR remote sensing, ocean optics, and marine biochemists to describe current and new active or passive remote sensing technologies (in situ, ship-based, airborne, and satellite), and collect the recent achievements of active and passive ocean optical remote sensing in recent years.

This Special Issue welcomes:

  • History or review of oceanographic LiDAR;
  • Multiple scattering LiDAR signal simulation;
  • Development of new algorithms of oceanographic LiDAR remote sensing;
  • Evaluation of oceanographic LiDAR inversion algorithms;
  • Applications of LiDAR techniques to retrieve profiles of biogeochemical parameters;
  • Applications of LiDAR techniques for plankton diurnal variation;
  • Mie scattering LiDAR;
  • HSRL LiDAR;
  • Fluorescence LiDAR;
  • Raman scattering LiDAR;
  • Imaging LiDAR;
  • Photon-counting LiDAR;
  • Brillouin scattering LiDAR;
  • LiDAR remote sensing of aerosol or winds above ocean;
  • LiDAR bathymetry;
  • Synergy of active and passive remote sensing;
  • Ground-based, ship-based, airborne, and space-borne LiDAR (CALIPO, ICEsat2, etc.);
  • Potential of new space-borne ocean LiDAR;
  • Any other issues related to remote sensing of seawater vertical structure (glider or bio-Argo, etc.) in the context of LiDAR measurements.

Dr. Peng Chen
Dr. Cédric Jamet
Prof. Dr. Junwu Tang
Dr. Martin A. Montes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LiDAR
  • remote sensing
  • ocean optics
  • ocean ecology
  • ocean color
  • development, validation and calibration of ocean LiDAR algorithms
  • aerosol–ocean interactions.

Published Papers (2 papers)

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Research

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Communication
High-Accuracy Spectral Measurement of Stimulated-Brillouin-Scattering Lidar Based on Hessian Matrix and Steger Algorithm
by
Zhiqiang Liu
,
Jie Sun
,
Xianda Zhang
,
Zhi Zeng
,
Yupeng Xu
,
Ningning Luo
,
Xingdao He
and
Jiulin Shi
Remote Sens. 2023, 15(6), 1511; https://doi.org/10.3390/rs15061511 - 09 Mar 2023
Viewed by 738
Abstract
The measurement accuracy of Brillouin scattering spectra is crucial for ocean remote sensing by Brillouin scattering lidar. Due to the limited resolution of ICCD cameras, the traditional processing methods remain at the pixel or partial sub-pixel level, which cannot meet the requirements of [...] Read more.
The measurement accuracy of Brillouin scattering spectra is crucial for ocean remote sensing by Brillouin scattering lidar. Due to the limited resolution of ICCD cameras, the traditional processing methods remain at the pixel or partial sub-pixel level, which cannot meet the requirements of high-performance lidar. In this paper, to extract the frequency shift with high precision from stimulated Brillouin scattering (SBS) lidar, a novel spectral processing method with sub-pixel recognition accuracy is proposed based on the Hessian matrix and Steger algorithm combined with the least square fitting method. Firstly, the Hessian matrix and Frangi filter are used for signal denoising. Then, the center points of SBS spectra at the sub-pixel level are extracted using the Steger algorithm and are connected and classified according to the signal type. On that basis, the frequency shifts of Brillouin scattering are calculated by using the center and radii of interference spectra after through fitting by the least squares method. Finally, the water temperatures are inverted by using the frequency shifts of Brillouin scattering. The results show that the processing method proposed in this paper can accurately calculate the frequency shift of Brillouin scattering. The measured errors of frequency shift are generally at an order of MHz, and the inversion accuracy of water temperature can be as low as 0.14 °C. This work is essential to the application for remote sensing the seawater parameters by using the Brillouin lidar technique. Full article
(This article belongs to the Special Issue Oceanographic Lidar in the Study of Marine Systems)
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Article
Carbon Air–Sea Flux in the Arctic Ocean from CALIPSO from 2007 to 2020
by
Siqi Zhang
,
Peng Chen
,
Zhenhua Zhang
and
Delu Pan
Remote Sens. 2022, 14(24), 6196; https://doi.org/10.3390/rs14246196 - 07 Dec 2022
Viewed by 1345
Abstract
Quantified research on the Arctic Ocean carbon system is poorly understood, limited by the scarce available data. Measuring the associated phytoplankton responses to air–sea CO2 fluxes is challenging using traditional satellite passive ocean color measurements due to low solar elevation angles. We [...] Read more.
Quantified research on the Arctic Ocean carbon system is poorly understood, limited by the scarce available data. Measuring the associated phytoplankton responses to air–sea CO2 fluxes is challenging using traditional satellite passive ocean color measurements due to low solar elevation angles. We constructed a feedforward neural network light detection and ranging (LiDAR; FNN-LID) method to assess the Arctic diurnal partial pressure of carbon dioxide (pCO2) and formed a dataset of long-time-series variations in diurnal air–sea CO2 fluxes from 2001 to 2020; this study represents the first time spaceborne LiDAR data were employed in research on the Arctic air–sea carbon cycle, thus providing enlarged data coverage and diurnal pCO2 variations. Although some models replace Arctic winter Chl-a with the climatological average or interpolated Chl-a values, applying these statistical Chl-a values results in potential errors in the gap-filled wintertime pCO2 maps. The CALIPSO measurements obtained through active LiDAR sensing are not limited by solar radiation and can thus provide ‘fill-in’ data in the late autumn to early spring seasons, when ocean color sensors cannot record data; thus, we constructed the first complete record of polar pCO2. We obtained Arctic FFN-LID-fitted in situ measurements with an overall mean R2 of 0.75 and an average RMSE of 24.59 µatm and filled the wintertime observational gaps, thereby indicating that surface water pCO2 is higher in winter than in summer. The Arctic Ocean net CO2 sink has seasonal sources from some continental shelves. The growth rate of Arctic seawater pCO2 is becoming larger and more remarkable in sectors with significant sea ice retreat. The combination of sea surface partial pressure and wind speed impacts the diurnal carbon air–sea flux variability, which results in important differences between the Pacific and Atlantic Arctic Ocean. Our results show that the diurnal carbon sink is larger than the nocturnal carbon sink in the Atlantic Arctic Ocean, while the diurnal carbon sink is smaller than the nocturnal carbon sink in the Pacific Arctic Ocean. Full article
(This article belongs to the Special Issue Oceanographic Lidar in the Study of Marine Systems)
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