Special Issue "Remote Sensing of Aquatic Ecosystem Health and Processes"

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

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Dr. Evangelos Spyrakos
Website
Guest Editor
University of Stirling, United Kingdom
Interests: His research is primarily focused on remote sensing of aquatic systems (including lakes, estuaries, coastal zones and open seas) in the context of environmental change, scientific/technological innovation and integration into strategies and approaches to environmental management and sustainable development.
Special Issues and Collections in MDPI journals
Dr. Claudia Giardino
Website
Guest Editor
CNR-IREA, Institute for Electromagnetic Sensing of the Environment, National Research Council
Interests: imaging spectroscopy and remote sensing of lakes; bio-optical modelling; shallow waters; water quality monitoring
Special Issues and Collections in MDPI journals
Dr. Vittorio E. Brando
Website
Guest Editor
CNR-ISMAR Institute of Marine Sciences, National Reseatch Council
Interests: earth observation; optical oceanography; coastal waters
Dr. Shenglei Wang

Guest Editor
Institute of Remote Sensing and Geographic Information System, Peking University, Beijing
Interests: water colour remote sensing; bio-optical properties and radiative transfer process in optically complex waters; spatio-temporal change of water quality and responses to climate change

Special Issue Information

Dear Colleagues,

The world’s aquatic ecosystems are vital components of the global biosphere, yet they are vulnerable to climate- and other human-induced change. They fulfil key functions in global biogeochemical cycles and are core to our water, food and energy security.  There is an obvious need for appropriate monitoring and management methods to protect these systems from deterioration and ensure their provision of goods and services. The rapidly increasing rate of data collection from different remote sensing platforms and sensors suitable for observing aquatic systems has promoted Earth observation as a more widely recognised source of information on a number of indicators of ecosystems’ condition at local and global scales. This Special Issue will focus on remote sensing advancements and applications for monitoring health, status and change as well as for studying ecosystem processes in aquatic systems such as rivers, lakes, transitional and coastal waters and open seas.

Dr. Evangelos Spyrakos
Dr. Claudia Giardino
Dr. Vittorio E. Brando
Dr. Shenglei Wang
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 papers will be 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 2200 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

  • Eutrophication, Trophic status
  • Primary production
  • Water quality
  • Sustainable Development Goals (including SDG 6, 14 and related)
  • Harmful algal blooms
  • Pollution
  • Marine Litter
  • Macrophytes
  • Sediment plumes
  • Disturbance
  • Phenology
  • Environmental change
  • In-situ characterisation and coupling with RS
  • Bio-optical modelling
  • Water continuum
  • Transitional ecosystems (lagoons, estuaries, coastal lakes, fjords)
  • Coral Reefs
  • Aquaculture, Fisheries

Published Papers (2 papers)

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Research

Open AccessArticle
Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme
Remote Sens. 2020, 12(11), 1849; https://doi.org/10.3390/rs12111849 - 08 Jun 2020
Cited by 1
Abstract
Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of [...] Read more.
Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including Q A A V 5 , Q A A V 6 , Q A A L 09 and Q A A M 14 , for the AHSI images with concurrent in situ measurements in four inland water bodies with a Z s d range of 0.3–4.5 m. The semi-analytical method with Q A A V 5 can yield the most accurate Z s d predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated Z s d had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated Z s d were 0.22, 0.46, and 0.24 m for Z s d of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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Open AccessArticle
Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm
Remote Sens. 2020, 12(9), 1516; https://doi.org/10.3390/rs12091516 - 09 May 2020
Abstract
The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted [...] Read more.
The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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