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Application of Ocean Colour Remote Sensing in Turbidity Monitoring
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Application of Ocean Colour Remote Sensing in Turbidity Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 4623

Special Issue Editors

Prof. Dr. Yuanzhi Zhang

E-Mail Website
Guest Editor
Institute of Asia-Pacific Studies and Center for Housing Innovations, Chinese University of Hong Kong, Central Ave, Hong Kong 999077, China
Interests: coastal water quality; suspended sediments matter; particle sizes of sediments; water components; Estuarine-coastal boundary; turbidity monitoring
Prof. Dr. Lin Li

E-Mail Website
Guest Editor
Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Interests: remote sensing; lunar and planetary geology; environmental sciences; data processing; radiative transfer models
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuanggen Jin

E-Mail Website
Guest Editor
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: GNSS meteorology; remote sensing; space/planetary exploration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhongfeng Qiu

E-Mail Website
Guest Editor
School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: ocean colour remote sensing; chlorophyll-a; turbidity; physical ocean parameters; data modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean colour remote sensing includes the mapping of surface temperature and chlorophyll-a, deriving the inherent optical properties (IOPs) of in-water constituents, and establishing relationships between the IOPs and apparent optical properties (AOPs). It has a wide range of applications in studying phytoplankton, detrital and sediment particles, turbidity, and other properties of oceanic, coastal, and inland water ecosystems. 

The relationship between turbidity and suspended solids concentration can be confounded by particle size and composition as well as water colour. Studies show that the variation in particle size can cause the turbidity to vary by a factor of four in the same concentration of suspended solids. In contrast, numerous strong correlations between turbidity and suspended solids concentration have been reported, indicating either an insignificant particle size variation or a covariation of particle size and suspended solid concentration. Although organic particles can result in turbidity values two to three times higher than mineral particles in concentration and particle size, short-term temporal variations from a purely organic to mineral particle load are rare in Nature, and thus organic matter in the particulate load will confound turbidity to a lesser degree. It is expected that an adequate relationship between turbidity and suspended solid concentration should exist in most situations.

To date, the application of ocean colour remote sensing in turbidity monitoring still faces challenges with in the determination of water components. This Special Issue on “Application of ocean colour remote sensing in turbidity monitoring” invites original research and review articles that focus on the monitoring and mapping of turbidity and the quantification of suspended solids concentration with remote sensing. The suggested topics are those relevant but not limited to the study of turbidity monitoring; data modelling; new algorithms; and the biogeochemical change of inland waters, river estuaries or across estuary–coastal water boundaries.

Prof. Dr. Yuanzhi Zhang
Prof. Dr. Lin Li
Prof. Dr. Shuanggen Jin
Prof. Dr. Zhongfeng Qiu
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. Water 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 2600 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

  • water components
  • suspended sediments
  • turbidity
  • ocean and coastal waters
  • inland waters (rivers and lakes or reservoirs)
  • estuarine–coastal water boundary
  • turbidity measured in the field
  • turbidity estimation from ocean colour data
  • data modelling
  • new algorithms
  • ocean colour remote sensing

Published Papers (2 papers)

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Research

19 pages, 6164 KiB  
Article
Shallow Water Bathymetry Mapping from ICESat-2 and Sentinel-2 Based on BP Neural Network Model
by Xiaozu Guo, Xiaoyi Jin and Shuanggen Jin
Water 2022, 14(23), 3862; https://doi.org/10.3390/w14233862 - 27 Nov 2022
Cited by 9 | Viewed by 2417
Abstract
Accurate shallow water bathymetry data are essential for coastal construction and management, marine traffic, and shipping. With the development of remote sensing satellites and sensors, the satellite-derived bathymetry (SDB) method has been widely used for bathymetry in shallow water areas. However, traditional satellite [...] Read more.
Accurate shallow water bathymetry data are essential for coastal construction and management, marine traffic, and shipping. With the development of remote sensing satellites and sensors, the satellite-derived bathymetry (SDB) method has been widely used for bathymetry in shallow water areas. However, traditional satellite bathymetry requires in-situ bathymetric data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with the advanced high-resolution topographic laser altimeter system (ATLAS) provides a new technical tool and makes up for the shortcomings of traditional bathymetric methods in shallow waters. In this study, a new method is proposed to automatically detect photons reflected from the shallow seafloor with ICESat-2 altimetry data. Two satellite bathymetry models were trained, to obtain shallow water depth from Sentinel-2 satellite images. First, sea surface and seafloor signal photons from ICESat-2 were detected in the Oahu (in the U.S. Hawaiian Islands) and St. Thomas (in the U.S. Virgin Islands) sampling areas, to obtain water depths along the surface track. The results show that the RMSE is between 0.35 and 0.71 m and the R2 is greater than 0.92, when compared to the airborne LiDAR bathymetry (ALB) data in the field. Second, the ICESat-2 bathymetric points from Oahu Island are used to train the Back Propagation (BP) neural network model and obtain the SDB. The RMSE is between 0.97 and 1.43 m and the R2 is between 0.90 and 0.96, which are better than the multi-band ratio model with RMSE of 1.03–1.57 m and R2 of 0.89–0.95. The results show that the BP neural network model can effectively improve bathymetric accuracy, when compared to the traditional multi-band ratio model. This approach can obtain shallow water bathymetry more easily, without the in-situ bathymetric data. Therefore, it extends to a greater extent with the free ICESat-2 and Sentinel-2 satellite data for bathymetry in shallow water areas, such as coastal, island and inland water bodies. Full article
(This article belongs to the Special Issue Application of Ocean Colour Remote Sensing in Turbidity Monitoring)
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16 pages, 2726 KiB  
Article
Assessing Sea Surface Temperatures Estimated from Fused Infrared and Microwave Data
by Jinyang Ni, Jiajun Feng, Runxia Sun and Yuanzhi Zhang
Water 2022, 14(21), 3357; https://doi.org/10.3390/w14213357 - 23 Oct 2022
Viewed by 1587
Abstract
Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave [...] Read more.
Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave observations are not blocked by clouds, allowing for data collection in all weather conditions, this technological tool is characterized by low spatial resolution. Conversely, infrared observations offer high resolution but are susceptible to cloud obscuration. Accordingly, a technique that effectively fuses microwave and infrared satellite observations into a high-resolution SST field with global coverage close to the actual distribution is of practical significance. This paper describes fusing MODIS infrared remote sensing and AMSR-2 microwave remote sensing SST data with an optimal interpolation (OI) approach to produce a high-resolution SST data. The study chose the coastal Kuroshio region of China to establish an appropriate scale for examining the spatial structure of SST and attaining a more realistic picture of SST observations and impacts. The included discussion of the sources of error in the fusion process provides a reference for improving the accuracy of fused marine remote sensing data. The study also compared the fused SST results and the current international mainstream multi-temporal resolution of the three using the OI algorithm. We compared the fusion product with ARGO data with and without typhoon impact to explore and practice the OI in SST fusion when evaluating the accuracy of different data in the case of external disturbance being present. The research results have great significance for improving regional SST forecast accuracy while ensuring the applicability of various approaches to fusing SST data by incorporating the influence of typhoons in the offshore region of the East China Sea (ECS). Implications for the future development of SST fusion data are also included in the discussion. Full article
(This article belongs to the Special Issue Application of Ocean Colour Remote Sensing in Turbidity Monitoring)
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