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Water
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Application of Satellite Remote Sensing in Water Quality Monitoring
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Application of Satellite Remote Sensing in Water Quality Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2920

Special Issue Editors

Prof. Dr. Juan Miguel Soria

E-Mail Website
Guest Editor
Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, Paterna, Spain
Interests: water quality; ecology; water resources management; water analysis; remote sensing; hydrology; water quality monitoring; freshwater ecology; aquatic ecosystems; lagoon plankton
Special Issues, Collections and Topics in MDPI journals
Dr. José Antonio Domínguez-Gómez

E-Mail Website
Guest Editor
School of Surveying, Geodesy and Cartography Engineering, Universidad Politécnica de Madrid, 28012 Madrid, Spain
Interests: monitoring water quality by remote sensing; monitoring blooms cyanobacteria by remote sensing; time series in water quality and climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the use of remote sensing as a tool for assessing the quality of the aquatic environment. The classical methodology allows the quality of several variables, such as water transparency, nutrients and photosynthetic pigments, to be determined after sampling and analytical campaigns. This requires time and availability for field work and frequent sampling in order to determine the spatial and temporal heterogeneity of the study site. Remote sensing allows us to obtain equations that relate the quality variables to the optical properties of water using empiral methods and to other results that are not directly related to these optical properties using machine learning methods; however, this can affect the water quality. This Special Issue welcomes the submission of papers that reflect studies on suspended matter, organic matter, eutrophication, massive algal growths, floating invasive plants and other topics of interest for applied research.

The Special Issue will accept theoretical papers describing new methodologies or empirical applications, case studies and experimental results that are related to freshwater, coastal or marine aquatic ecosystems. In particular, we welcome studies that consider lakes, lagoons, reservoirs, estuaries and transitional waters.

We invite researchers to submit manuscripts that enable us to advance our understanding of the utilization of Satellite Remote Sensing in the monitoring of water quality. The scope of this Special Issue covers all aspects of the physics and ecology relevant to this field.

Prof. Dr. Juan Miguel Soria
Dr. José Antonio Domínguez-Gómez
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

  • remote sensing approaches
  • time series
  • environmental changes
  • water quality
  • human impacts: agriculture, aquaculture, tourism
  • eutrophication

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Published Papers (2 papers)

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Research

23 pages, 18000 KiB  
Article
A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China
by Hao Chen, Changmiao Tan, Huanhua Peng, Wentao Yang and Lelin Li
Water 2024, 16(10), 1319; https://doi.org/10.3390/w16101319 - 7 May 2024
Viewed by 1259
Abstract
Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring [...] Read more.
Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable. Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing in Water Quality Monitoring)
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24 pages, 10455 KiB  
Article
Prediction of Diffuse Attenuation Coefficient Based on Informer: A Case Study of Hangzhou Bay and Beibu Gulf
by Rongyang Cai, Miao Hu, Xiulin Geng, Mohammed K. Ibrahim and Chunhui Wang
Water 2024, 16(9), 1279; https://doi.org/10.3390/w16091279 - 29 Apr 2024
Viewed by 1283
Abstract
Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes [...] Read more.
Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management. Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing in Water Quality Monitoring)
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