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Recent Advances in Water Quality Monitoring
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Recent Advances in Water Quality Monitoring

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3332

Special Issue Editors

Dr. Alexandra Pavlidou

E-Mail Website
Guest Editor
Hellenic Centre for Marine Research, Institute of Oceanography, Athens, Greece
Interests: water quality assessment; eutrophication; nutrients; biogeochemical processes; marine pollution
Prof. Dr. Ibrahim Hoteit

E-Mail Website
Guest Editor
Climate Change Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Interests: ocean modelling; data assimilation; remote sensing; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Martins

E-Mail Website
Guest Editor
Department of Oceanography and Fisheries, University of the Azores, Azores, Portugal
Interests: coupled physical-biological processes in the ocean; climate change; remote sensing; satellite oceanography; open ocean biodiversity and dynamics; marine pollution
Dr. Ioanna Varkitzi

E-Mail Website
Guest Editor
Hellenic Centre for Marine Research, Institute of Oceanography, Athens, Greece
Interests: water quality assessment; harmful algal blooms (HABs) dynamics; eutrophication processes; pelagic habitat status
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water-quality monitoring in open and near-shore marine environments requires the assessment of multiple stressors and ecosystem responses. The implementation of environmental policies related to water-quality monitoring is a demanding task for several agencies and competent authorities in order to achieve Good Environmental Status (GES) for diverse applications, such as recreation, drinking, fishing, aquaculture, industry, etc. However, it is economically and logistically challenging to extensively monitor the marine environment. Although in situ measurements are the most reliable for water-quality assessment, it is usually time-consuming, expensive, and labor-intensive, especially over large ocean environments, to collect these data. Moreover, the gaps in situ data availability and the limited access or dissemination of existing data in many countries around the world increase the complexity of water-quality monitoring. In such a context, the scientific community is trying to overcome these gaps by introducing novel technologies for monitoring ocean water quality. The use of remote sensing (RS) tools, numerical ocean modeling, and artificial intelligence (AI) tools in support of water-quality monitoring and environmental assessment has gained significant importance in the last decade, whereas the list of relevant services continues to expand. Although RS techniques may not be as accurate as in situ measurements, they can effectively mitigate most of the limitations of field-based data collection, providing cost-effective and frequent observations over the marine environment to facilitate effective water-quality monitoring over time.

The main goal of this Special Issue is to provide a scientific platform to discuss recent advances in the application of RS systems and modeling and AI techniques to monitor the water quality parameters in the marine environment. This Special Issue will attempt to elucidate the current progress in this field by collecting new techniques and technologies, case studies, and showcases for monitoring water quality in diverse marine areas and water bodies. Applications of various RS techniques and products and model-derived parameters, such as sea surface temperature, salinity, currents, chlorophyll-a, primary production, phytoplankton functional types, suspended matter, turbidity, transparency, reflectance, inorganic nutrients, dissolved oxygen, CDOM, oil spills, plastics, etc., are also of interest.

Authors are encouraged to submit contributions on the advances in water quality assessment. Topics can include (but are not limited to) the following:

  • Remote sensing products for water quality monitoring (chlorophyll-a, turbidity, harmful algal blooms, etc.);
  • Marine litter detection and tracking;
  • UAVs and other new sensing platforms’ feasibility as water quality solutions;
  • Integration and assimilation of satellite data into models of marine ecosystem dynamics;
  • High spatial and temporal resolution remote sensing for studying ecosystem dynamics;
  • Time series analysis and trends;
  • Operational remote sensing products and their integration into classical monitoring programs;
  • Generating higher-level ecosystem indicators from satellite data;
  • Water quality analysis at national, continental, or global scale;
  • Combining coastal and terrestrial remote sensing products to investigate water-catchment interactions.

Dr. Alexandra Pavlidou
Prof. Dr. Ibrahim Hoteit
Dr. Ana Martins
Dr. Ioanna Varkitzi
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

  • water quality assessment
  • marine pollution
  • eutrophication
  • phytoplankton
  • HABs
  • marine litter
  • oil spills
  • dumping of dredge spoil
  • eddies and ecosystem dynamics in open waters

Published Papers (3 papers)

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Research

37 pages, 24874 KiB  
Article
Earth Observation-Based Cyanobacterial Bloom Index Testing for Ecological Status Assessment in the Open, Coastal and Transitional Waters of the Baltic and Black Seas
by Diana Vaičiūtė, Yevhen Sokolov, Martynas Bučas, Toma Dabulevičienė and Olga Zotova
Remote Sens. 2024, 16(4), 696; https://doi.org/10.3390/rs16040696 - 16 Feb 2024
Viewed by 576
Abstract
The use of Earth Observation (EO) for water quality monitoring has substantially raised in the recent decade; however, harmonisation of EO-based indicators across the seas to support environmental policies is in great demand. EO-based Cyanobacterial Bloom Index (CyaBI) originally developed for open waters, [...] Read more.
The use of Earth Observation (EO) for water quality monitoring has substantially raised in the recent decade; however, harmonisation of EO-based indicators across the seas to support environmental policies is in great demand. EO-based Cyanobacterial Bloom Index (CyaBI) originally developed for open waters, was tested for transitional and coastal waters of the Lithuanian Baltic Sea and the Ukrainian Black Sea during 2006–2019. Among three tested neural network-based processors (FUB-CSIRO, C2RCC, standard Level-2 data), the FUB-CSIRO applied to Sentinel-3 OLCI images was the most appropriate for the retrieval of chlorophyll-a in both seas (R2 = 0.81). Based on 147 combined MERIS and OLCI synoptic satellite images for the Baltic Sea and 234 for the Black Sea, it was shown that the CyaBI corresponds to the eutrophication patterns and trends over the open, coastal and transitional waters. In the Baltic Sea, the cyanobacteria blooms mostly originated from the central part and the outflow of the Curonian Lagoon. In the Black Sea, they occurred in the coastal region and shelf zone. The recent decrease in bloom presence and its severity were revealed in the areas with riverine influence and coastal waters. Intensive blooms significantly enhanced the short-term increase in sea surface temperature (mean ≤ 0.7 °C and max ≤ 7.0 °C) compared to surrounding waters, suggesting that EO data originating from thermal infrared sensors could also be integrated for the ecological status assessment. Full article
(This article belongs to the Special Issue Recent Advances in Water Quality Monitoring)
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24 pages, 20992 KiB  
Article
An Integrated Framework for Remote Sensing Assessment of the Trophic State of Large Lakes
by Dinghua Meng, Jingqiao Mao, Weifeng Li, Shijie Zhu and Huan Gao
Remote Sens. 2023, 15(17), 4238; https://doi.org/10.3390/rs15174238 - 29 Aug 2023
Cited by 1 | Viewed by 1328
Abstract
The trophic state is an important factor reflecting the health state of lake ecosystems. To accurately assess the trophic state of large lakes, an integrated framework was developed by combining remote sensing data, field monitoring data, machine learning algorithms, and optimization algorithms. First, [...] Read more.
The trophic state is an important factor reflecting the health state of lake ecosystems. To accurately assess the trophic state of large lakes, an integrated framework was developed by combining remote sensing data, field monitoring data, machine learning algorithms, and optimization algorithms. First, key meteorological and environmental factors from in situ monitoring were combined with remotely sensed reflectance data and statistical analysis was used to determine the main factors influencing the trophic state. Second, a trophic state index (TSI) inversion model was constructed using a machine learning algorithm, and this was then optimized using the sparrow search algorithm (SSA) based on a backpropagation neural network (BP-NN) to establish an SSA-BP-NN model. Third, a typical lake in China (Hongze Lake) was chosen as the case study. The application results show that, when the key environmental factors (pH, temperature, average wind speed, and sediment content) and the band combination data from Sentinel-2/MSI were used as input variables, the performance of the model was improved (R2 = 0.936, RMSE = 1.133, MAPE = 1.660%, MAD = 0.604). Compared with the performance prior to optimization (R2 = 0.834, RMSE = 1.790, MAPE = 2.679%, MAD = 1.030), the accuracy of the model was improved by 12.2%. It is worth noting that this framework could accurately identify water bodies in different trophic states. Finally, based on this framework, we mapped the spatial distribution of TSI in Hongze Lake in different seasons from 2019 to 2020 and analyzed its variation characteristics. The framework can combine regional special feature factors influenced by a complex environment with S-2/MSI data to achieve an assessment accuracy of over 90% for TSI in sensitive waters and has strong applicability and robustness. Full article
(This article belongs to the Special Issue Recent Advances in Water Quality Monitoring)
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19 pages, 12546 KiB  
Article
A Probabilistic Approach to Mapping the Contribution of Individual Riverine Discharges into Liverpool Bay Using Distance Accumulation Cost Methods on Satellite Derived Ocean-Colour Data
by Richard Heal, Lenka Fronkova, Tiago Silva, Kate Collingridge, Richard Harrod, Naomi Greenwood and Michelle J. Devlin
Remote Sens. 2023, 15(14), 3666; https://doi.org/10.3390/rs15143666 - 23 Jul 2023
Viewed by 903
Abstract
Assessments of the water quality in coastal zones often rely on indirect indicators from contributing river inputs and the neighbouring ocean. Using a novel combination of distance accumulation cost methods and an ocean-colour product derived from SENTINEL-3 data, we developed a probabilistic method [...] Read more.
Assessments of the water quality in coastal zones often rely on indirect indicators from contributing river inputs and the neighbouring ocean. Using a novel combination of distance accumulation cost methods and an ocean-colour product derived from SENTINEL-3 data, we developed a probabilistic method for the assessment of dissolved inorganic nitrogen (DIN) in Liverpool Bay (UK) for the period from 2017 to 2020. Using our approach, we showed the annual and monthly likelihood of DIN exposure from its 12 major contributory rivers. Furthermore, we generated monthly risk maps showing the probability of DIN exposure from all rivers, which revealed a seasonal variation of extent and location around the bay. The highest likelihood of high DIN exposure throughout the year was in the estuarine regions of the Dee, Mersey, and Ribble, along with near-shore areas along the north Wales coast and around the mouth of the rivers Mersey and Ribble. There were seasonal changes in the risk of DIN exposure, and this risk remained high all year for the Mersey and Dee estuary regions. In contrast, for the mouth and near the coastal areas of the Ribble, the DIN exposure decreased in spring, remained low during the summer and early autumn, before displaying an increase during winter. Our approach offers the ability to assess the water quality within coastal zones without the need of complex hydrodynamic models, whilst still having the potential to apportion nutrient exposure to specific riverine inputs. This information can help to prioritise how direct mitigation strategies can be applied to specific river catchments, focusing the limited resources for coastal zone and river basin management. Full article
(This article belongs to the Special Issue Recent Advances in Water Quality Monitoring)
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