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Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

Abstract

In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain.

R. Priyadarshini and B. Sudhakara—Equal contribution.

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Acknowledgements

The authors gratefully acknowledge the computational resources made available as part of the AI for Earth Grant funded by Microsoft.

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    R. Priyadarshini, B. Sudhakara, S. Sowmya Kamath & Shrutilipi Bhattacharjee

  2. Department of Water and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    U. Pruthviraj

  3. Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    K. V. Gangadharan

Authors
  1. R. Priyadarshini
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  2. B. Sudhakara
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  3. S. Sowmya Kamath
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  4. Shrutilipi Bhattacharjee
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  5. U. Pruthviraj
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  6. K. V. Gangadharan
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Corresponding author

Correspondence to R. Priyadarshini .

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Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Priyadarshini, R., Sudhakara, B., Sowmya Kamath, S., Bhattacharjee, S., Pruthviraj, U., Gangadharan, K.V. (2023). Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_46

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