A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data Article Swipe
Anastazia Daniel Msusa
,
Dalin Jiang
,
Bunkei Matsushita
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14040868
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14040868
Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kd(λ)) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated ZSD matched well with the in situ measured ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.
Related Topics
Concepts
Visibility
Remote sensing
Algorithm
Underwater
Outlier
Secchi disk
Environmental science
Imaging spectrometer
Wavelength
Attenuation coefficient
Attenuation
Water quality
Spectrometer
Computer science
Mathematics
Statistics
Optics
Physics
Geology
Chemistry
Eutrophication
Ecology
Oceanography
Nutrient
Biology
Organic chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14040868
- https://www.mdpi.com/2072-4292/14/4/868/pdf?version=1644993720
- OA Status
- gold
- Cited By
- 6
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213227573
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213227573Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14040868Digital Object Identifier
- Title
-
A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-02-11Full publication date if available
- Authors
-
Anastazia Daniel Msusa, Dalin Jiang, Bunkei MatsushitaList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14040868Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/4/868/pdf?version=1644993720Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/14/4/868/pdf?version=1644993720Direct OA link when available
- Concepts
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Visibility, Remote sensing, Algorithm, Underwater, Outlier, Secchi disk, Environmental science, Imaging spectrometer, Wavelength, Attenuation coefficient, Attenuation, Water quality, Spectrometer, Computer science, Mathematics, Statistics, Optics, Physics, Geology, Chemistry, Eutrophication, Ecology, Oceanography, Nutrient, Biology, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2024: 2, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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