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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 4563

Special Issue Editors

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
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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

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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 24 | Viewed by 3594
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)
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