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Remote Sensing of Water Bodies
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Remote Sensing of Water Bodies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 5002

Special Issue Editors

Prof. Dr. Ronghua Ma

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing of inland lakes; water quality; water environment; aquatic ecology; machine learning; GIS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chunqiao Song

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: hydrological remote sensing; remote sensing of resources and the environment; surface water resources and global change; impact of climate change on Tibet Plateau
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inland and coastal water bodies are crucial for various services for human societies. Under the context of a changing climate and intensified human interventions, the quality and quantity of water bodies has evidently been changing. Satellite remote sensing is an efficient and crucial tool for monitoring and sustainable management of those water resources. However, it is still very challenging for algorithm development and various applications due to the sensor’s electromagnetic interaction with the atmosphere and complex substances in waters. In recent years, research on remote sensing of inland water color has greatly increased. However, the water mass has to some extent been less focused on. Meawhile, the rapid development of mathematic techniques (e.g., machine learning) and cloud computation platforms (e.g., Google Earth Engine) provides new opportunities to improve the capacity of satellite remote sensing for water monitoring. There is a clear need to share approaches and new ideas that can be used to strenthen the approach of investigating water quality or water storage.

To meet this urgent need, a Special Issue on “Monitoring Waters from Space” has been accepted by the leading international journal Sensors, to address the technical challenges for satellite monitoring or estimating of water bodies.

We sincerely solicit your contributions in this field to our Sensors Special Issue. Research or review articles with respect to the following topics are welcome:

  • Waterbody delineation from remote sensing imagery;
  • Water volume estimation by remote sensing;
  • Satellite monitoring of inland water resources;
  • Remote sensing retrival of water corlor/quality parameters;
  • Machine learning applications on remote sensing of water environment;
  • Google Earth Engine-based remote sensing of water environment;
  • Satellite mapping of aquatic macrophytes in inland waters;
  • Long-term satellite monitoring of watershed LUCC linked to water quality or storage.

Dr. Ronghua Ma
Dr. Chunqiao Song
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. Sensors 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 volume
  • water quality
  • remote sensing
  • algorithm development
  • environmental monitoring
  • machine learning

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Published Papers (1 paper)

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Research

29 pages, 8785 KiB  
Article
Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China
by Jing Zhao, Fujie Zhang, Shuisen Chen, Chongyang Wang, Jinyue Chen, Hui Zhou and Yong Xue
Sensors 2020, 20(23), 6911; https://doi.org/10.3390/s20236911 - 3 Dec 2020
Cited by 31 | Viewed by 3973
Abstract
Accurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality [...] Read more.
Accurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality evaluation method based on the Markov model and remote sensing retrieval is proposed to realize the innovation of large-scale spatial monitoring across administrative boundaries. Additionally, to explore the spatiotemporal characteristics and driving factors of TSS, a new three-band remote sensing model of TSS was built by regression analysis for the inland reservoir using the synchronous field spectral data, water quality samples and remote sensing data in the trans-provincial Hedi Reservoir in the Guangdong and Guangxi Provinces of South China. The results show that: (1) The three-band model based on the OLI sensor explained about 82% of the TSS concentration variation (R2=0.81, N=34,  p value<0.01) with an acceptable validation accuracy (RMSE=6.24 mg/L,MRE=18.02%, N=15), which is basically the first model of its kind available in South China. (2) The TSS concentration has spatial distribution characteristics of high upstream and low downstream, where the average TSS at 31.54 mg/L in the upstream are 2.5 times those of the downstream (12.55 mg/L). (3) Different seasons and rainfall are important factors affecting the TSS in the upstream cross-border area, the TSS in the dry season are higher with average TSS of 33.66 mg/L and TSS are negatively correlated with rainfall from upstream mankind activity. Generally, TSS are higher in rainy seasons than those in dry seasons. However, the result shows that TSS are negatively correlated with rainfall, which means human activities have higher impacts on water quality than climate change. (4) The Markov dynamic evaluation results show that the water quality improvement in the upstream Shijiao Town is the most obvious, especially in 2018, the improvement in the water quality level crossed three levels and the TSS were the lowest. This study provided a technical method for remote sensing dynamic monitoring of water quality in a large reservoir, which is of great significance for remediation of the water environment and the effective evaluation of the river and lake chief system in China. Full article
(This article belongs to the Special Issue Remote Sensing of Water Bodies)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Location of the study area (Hedi Reservoir) and the location of the Hedi Reservoir Basin across Zhanjiang, Guangdong Province, and Yulin, Guangxi Province, in China, and (<b>b</b>) location of in situ data labeled by black stars and the distribution of sampling stations where different color lines represent different boundaries of towns.</p> Full article ">Figure 2
<p>The calibration and validation of the TSS remote sensing estimation model between simulated OLI-based <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> and in situ measurement data are shown in the figure, where (<b>a</b>) shows the model calibrated by the band ratio algorithm, and (<b>b</b>) shows the model of the three-band algorithm. Plots of measured vs. model-estimated TSS in Hedi Reservoir with a 1:1 fit line (red dotted line), where (<b>c</b>) is the result of validating the band ratio model, and (<b>d</b>) is the validation result of the three-band model.</p> Full article ">Figure 3
<p>Estimated TSS concentrations based on the three-band model from Landsat OLI imagery in Hedi Reservoir on 16 October 2015, where the different color lines stand for towns over the reservoir basin (<b>a</b>), and comparison between the in situ measured and OLI imagery-retrieved TSS concentrations (<b>b</b>). The color scale is the legend of the TSS concentrations, in mg/L.</p> Full article ">Figure 4
<p>A number of 35 measured spectra of typical water collected by ASD in the 2015 cruise over Hedi Reservoir, where the different color lines represent the different spectra of 35 water samples.</p> Full article ">Figure 5
<p>The spatial distribution of TSS concentration based on OLI imagery in Hedi Reservoir, where different colored lines represent different boundaries of towns: (<b>a</b>) mapping of the averaged TSS concentration in Hedi Reservoir from 2014 to 2018, and (<b>b</b>) the comparison of the annual average TSS concentration in different areas within the reservoir. The color scale is the legend of the average TSS concentrations, in mg/L. The location of the red star in the picture is the government of Shijiao Town.</p> Full article ">Figure 6
<p>Percent distribution of annual average concentrations of TSS in Shijiao Town from 2014 to 2018, including levels with thresholds of 20, 30, 50 and 80 mg/L.</p> Full article ">Figure 7
<p>The spatial distribution map for the estimated concentration of TSS on an annual average in dry and flood seasons of Hedi Reservoir from 2014 to 2018 is mapped as shown in the figure, among which (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) are in the dry season within 5 years, and the rest (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) fall into the flood season. The color scale is the legend of the average TSS concentrations, in mg/L.</p> Full article ">Figure 7 Cont.
<p>The spatial distribution map for the estimated concentration of TSS on an annual average in dry and flood seasons of Hedi Reservoir from 2014 to 2018 is mapped as shown in the figure, among which (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) are in the dry season within 5 years, and the rest (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) fall into the flood season. The color scale is the legend of the average TSS concentrations, in mg/L.</p> Full article ">Figure 8
<p>Comparison of annual average TSS concentrations in the reservoir area of Shijiao Township from 2014 to 2018 in different seasons.</p> Full article ">Figure 9
<p>Comparison of time series variation of TSS concentration and daily precipitation in Shijiao Town, where the average TSS concentration is signed by orange triangles and the daily precipitation is signed by blue dots.</p> Full article ">Figure 10
<p>Historical view of the Google Earth image showing the polluted water environment in Hedi Reservior caused by different anthropogenic activities: (<b>a</b>) shows the status of large-scale illegal sand mining activities before the river chief system was implemented, (<b>b</b>) indicates that sand mining activities are effectively restricted after the execution of the river chief system policy and (<b>c</b>,<b>d</b>) depict the extensive area of eucalyptus planting with frequent cutting. All the red circled areas in Figures (<b>a</b>,<b>b</b>) indicate changes in sand mining vessels in the same area of the reservoir before and after implementation of the river chief system policy; (<b>c</b>,<b>d</b>) reflect the changed progress in eucalyptus planting and cutting.</p> Full article ">Figure 11
<p>The evaluation results of the progress degree of TSS in the Shijiao Town area of Hedi Reservoir from 2014 to 2018. The blue part is based on the region-averaged TSS concentration in the Shijiao Town area, and the orange part is based on the pixel-based TSS concentration in the Shijiao Town area.</p> Full article ">Figure 12
<p>The spatial distribution map of water quality variation calculated by the progress degree combining the Markov model and the remote sensing method from 2014 to 2018 in Hedi Reservoir, where (<b>a</b>–<b>e</b>) show the annual spatial variation distribution based on the progress degree and (<b>f</b>) presents the spatial variation within the whole five years. The color scale marked as <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>L</mi> </mrow> </semantics></math> indicates the difference in the number of changes in the two TSS levels during the change in the TSS level status. It means that the TSS level has increased and the water quality has improved when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>L</mi> </mrow> </semantics></math> is greater than 0, otherwise the TSS level has decreased and the water quality has deteriorated. Especially, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>L</mi> </mrow> </semantics></math> is 0, which means that the TSS level has not changed and the water quality remains stable.</p> Full article ">Figure 12 Cont.
<p>The spatial distribution map of water quality variation calculated by the progress degree combining the Markov model and the remote sensing method from 2014 to 2018 in Hedi Reservoir, where (<b>a</b>–<b>e</b>) show the annual spatial variation distribution based on the progress degree and (<b>f</b>) presents the spatial variation within the whole five years. The color scale marked as <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>L</mi> </mrow> </semantics></math> indicates the difference in the number of changes in the two TSS levels during the change in the TSS level status. It means that the TSS level has increased and the water quality has improved when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>L</mi> </mrow> </semantics></math> is greater than 0, otherwise the TSS level has decreased and the water quality has deteriorated. Especially, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>L</mi> </mrow> </semantics></math> is 0, which means that the TSS level has not changed and the water quality remains stable.</p> Full article ">
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