A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data
Water transparency (or Secchi disk depth:<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi><...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/868 |
| _version_ | 1797476803402530816 |
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| author | Anastazia Daniel Msusa Dalin Jiang Bunkei Matsushita |
| author_facet | Anastazia Daniel Msusa Dalin Jiang Bunkei Matsushita |
| author_sort | Anastazia Daniel Msusa |
| collection | DOAJ |
| description | Water transparency (or Secchi disk depth:<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 (<i>K<sub>d</sub></i>(<i>λ</i>)) will occur for each water type. The performance of the proposed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> matched well with the in situ measured <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>, with a MAPE of 15%. The developed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> estimation algorithm can probably be applied to different optical water types due to its semianalytical features. |
| first_indexed | 2024-03-09T21:08:53Z |
| format | Article |
| id | doaj.art-1d01b9b8e5ab47d5b92b5da835feb8b4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:53Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1d01b9b8e5ab47d5b92b5da835feb8b42023-11-23T21:53:21ZengMDPI AGRemote Sensing2072-42922022-02-0114486810.3390/rs14040868A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS DataAnastazia Daniel Msusa0Dalin Jiang1Bunkei Matsushita2Graduate School of Life and Environmental Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba 305-8572, JapanEarth and Planetary Observation Sciences (EPOS), Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling FK9 4LA, UKFaculty of Life and Environmental Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba 305-8572, JapanWater transparency (or Secchi disk depth:<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 (<i>K<sub>d</sub></i>(<i>λ</i>)) will occur for each water type. The performance of the proposed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> matched well with the in situ measured <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>, with a MAPE of 15%. The developed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>S</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> estimation algorithm can probably be applied to different optical water types due to its semianalytical features.https://www.mdpi.com/2072-4292/14/4/868secchi disk depthwater qualitywater type classificationsemianalytical modelsMERIS |
| spellingShingle | Anastazia Daniel Msusa Dalin Jiang Bunkei Matsushita A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data Remote Sensing secchi disk depth water quality water type classification semianalytical models MERIS |
| title | A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data |
| title_full | A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data |
| title_fullStr | A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data |
| title_full_unstemmed | A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data |
| title_short | A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data |
| title_sort | semianalytical algorithm for estimating water transparency in different optical water types from meris data |
| topic | secchi disk depth water quality water type classification semianalytical models MERIS |
| url | https://www.mdpi.com/2072-4292/14/4/868 |
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