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From Observations to End-User Engagement: Operational Applications of Remote Sensing to Inland and Coastal Waters

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 5243

Special Issue Editors


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Guest Editor
Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
Interests: water quality; validation; radiative transfer; inland and coastal waters; science valorization
Special Issues, Collections and Topics in MDPI journals
WaterInsight, Water Insight, Marijkeweg 22, 6709 PG Wageningen, The Netherlands
Interests: water quality; remote sensing; aquatic system; industry; user-driven applications

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to submit original papers on the topic “From Observations to End-User Engagement: Operational Applications of Remote Sensing to Inland and Coastal Waters.”

With the operational data streams from the Sentinel missions and the Copernicus program of the European Space Agency and the maturity of emerging observations from miniature satellites and unmanned aerial vehicles, remote sensing technology is moving towards long-term sustainable data streaming, forming the basis for its operational use. In particular, setting up remote sensing (RS) services for inland and coastal waters provides an instrument for early warning and mitigation on extended spatiotemporal scales. However, the added value of any RS-based service is determined, primarily, by its uptake by the end-users, e.g., managers, governmental organizations, private companies, and/or the general public.

With your original contributions, this Special Issue will answer vital questions on the added values of observation and end-user engagement:

  1. Observation
    1. What are the recent advances in remote sensing applications to inland and coastal waters? For this question the authors are invited to address the following topics:
      1. Technology: new sensors and sensing techniques
      2. Techniques for triple sensors collocation (onsite biophysical sensors + radiance sensors + satellites) addressing spatial and temporal mismatches and uncertainty
      3. Citizen and data science
      4. Predictions and early warning systems
      5. Effect of climate change and climate change projections
  1. End-user engagement
    1. What are the users’ requirements for better and tailored services? For this question the authors are invited to address:
      1. Users’ requirements from different perspectives: instrumentation, application, and/or accuracy
      2. Sustainability of the users’ engagement and enhancing the uptake of remote sensing services

The Special Issue will accept review and research papers with a focus on remote sensing applications for inland and coastal waters.

Dr. Mhd. Suhyb Salama
Dr. Steef Peters
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

  • Inland
  • Coastal
  • Water quality
  • Remote sensing
  • Earth observation
  • Citizen science
  • Date science
  • User requirements
  • Predictions
  • Early warning systems
  • Climate change

Published Papers (2 papers)

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Research

17 pages, 3551 KiB  
Article
HY-1C/D Reveals the Chlorophyll-a Concentration Distribution Details in the Intensive Islands’ Waters and Its Consistency with the Distribution of Fish Spawning Ground
by Lina Cai, Menghan Yu, Xiaojun Yan, Yongdong Zhou and Songyu Chen
Remote Sens. 2022, 14(17), 4270; https://doi.org/10.3390/rs14174270 - 30 Aug 2022
Cited by 5 | Viewed by 1417
Abstract
Chlorophyll-a (Chl-a) change details derived from HY-1C/D images in the waters of the Zhoushan archipelago were analyzed. A new Chl-a inverse model was built based on the relationship between the in situ Chl-a and the combination of red, blue and green bands of [...] Read more.
Chlorophyll-a (Chl-a) change details derived from HY-1C/D images in the waters of the Zhoushan archipelago were analyzed. A new Chl-a inverse model was built based on the relationship between the in situ Chl-a and the combination of red, blue and green bands of the coastal zone imager (CZI). Chl-a as well as fishery resources were analyzed. The results showed the following. (1) The Chl-a concentration in the waters of the Zhoushan archipelago was mainly in the range of 0.5~6 μg/L. High Chl-a area distributed in the west side of the study area, with a value of 3.5~5.5 μg/L. The Chl-a concentration in the east side of the study area was relatively lower, with a value of 0.5~2 μg/L. Chl-a around the islands was higher than that in the area far away from the islands. In addition, Chl-a concentration increased obviously downstream of the island. (2) The spawning ground of many fish in the waters of the Zhoushan archipelago was abundant, and its spatial-temporal variation was consistent with the change of Chl-a. (3) The islands interacted with the current, inducing upwelling upstream and vortex streets downstream. The complex hydrodynamic environment promoted a vertical exchange of water bodies, thereby resulting in an increase in suspended sediment concentration, nutrients, Chl-a and attracting fish. Full article
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16 pages, 4767 KiB  
Article
A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors
by Shijie Zhu and Jingqiao Mao
Remote Sens. 2021, 13(13), 2498; https://doi.org/10.3390/rs13132498 - 26 Jun 2021
Cited by 12 | Viewed by 2662
Abstract
To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a [...] Read more.
To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere. Full article
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