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Optical Water Types and Their Importance in Predicting Water Quality Metrics by Satellite Imagery.

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Title: Optical Water Types and Their Importance in Predicting Water Quality Metrics by Satellite Imagery.
Authors: Brezonik, Patrick L.1 (AUTHOR), Olmanson, Leif G.2 (AUTHOR) olman002@umn.edu
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1818. 26p.
Subjects: Water quality, Spectral reflectance, Water quality monitoring, Normalized difference vegetation index, Remote-sensing images, K-means clustering, Dissolved organic matter
Abstract: Highlights: What are the main findings? Pre-classification of waterbodies into optical water types (OWTs) based on integrative, satellite-based metrics produced better predictions of the water quality variables Secchi depth and colored dissolved organic matter (CDOM) than predictions based on an unclassified dataset of 109 Minnesota and Wisconsin waterbodies. The integrative metric of reflectance spectral shape, apparent visible wavelength (AVW), had distinct relationships with three common water quality variables, Secchi depth, chlorophyll-a, and colored dissolved organic matter (CDOM). AVW was also correlated with another integrative metric of reflectance spectra, the normalized difference index (NDI) for green and red wavelengths. What are the implications of the main findings? The accuracy of water quality data retrieved from satellite imagery can be improved by straightforward methods to pre-classify waterbodies into optical water types using data that can be calculated directly from satellite reflectance data. AVW is an appropriate metric to use in developing OWTs from multidimensional, satellite-derived data. For lakes in the region of study, OWTs can be derived using just two metrics, AVW and a metric of spectral magnitude, such as the trapezoidal area at red, green, and blue bands, ARGB, or similar metrics. Pre-classification of lakes into optical water types (OWTs) is considered a useful step in analyzing satellite-based reflectance data. We used a dataset of 109 reflectance hyperspectra from Minnesota and Wisconsin lakes and rivers to evaluate the usefulness of pre-classification to improve the retrieval of water quality information from satellite data. Three OWT classes were derived from the dataset by K-means clustering using three integrative metrics of reflectance spectral shape and magnitude as clustering variables. Values of the three metrics can be determined from satellite reflectance data as well as hyperspectral data. The OWT classes had distinct water quality characteristics in terms of Secchi depth, chlorophyll-a, and colored dissolved organic matter (CDOM). Algorithms used to retrieve values of the variables from simulated Sentinel-2 band reflectance data usually yielded more accurate predictions when computed separately for each class than when computed for the entire dataset, although exceptions were found for some fitting metrics and models and results for chlorophyll-a were not definitive. The three water quality variables were related in distinct ways to the integrative shape metric of reflectance spectra, apparent visible wavelength (AVW), supporting its use to develop OWTs to organize waterbodies into water quality classes. AVW was correlated (r = 0.933) with the integrative metric, normalized difference index at green and red wavelengths (NDI). Based on that result, we found that OWTs developed using just two variables, AVW and a metric of spectral magnitude, were nearly the same as classifications using all three integrative metrics. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optical Water Types and Their Importance in Predicting Water Quality Metrics by Satellite Imagery.
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  Data: <searchLink fieldCode="AR" term="%22Brezonik%2C+Patrick+L%2E%22">Brezonik, Patrick L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Olmanson%2C+Leif+G%2E%22">Olmanson, Leif G.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> olman002@umn.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1818. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Water+quality%22">Water quality</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+reflectance%22">Spectral reflectance</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+monitoring%22">Water quality monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Dissolved+organic+matter%22">Dissolved organic matter</searchLink>
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  Data: Highlights: What are the main findings? Pre-classification of waterbodies into optical water types (OWTs) based on integrative, satellite-based metrics produced better predictions of the water quality variables Secchi depth and colored dissolved organic matter (CDOM) than predictions based on an unclassified dataset of 109 Minnesota and Wisconsin waterbodies. The integrative metric of reflectance spectral shape, apparent visible wavelength (AVW), had distinct relationships with three common water quality variables, Secchi depth, chlorophyll-a, and colored dissolved organic matter (CDOM). AVW was also correlated with another integrative metric of reflectance spectra, the normalized difference index (NDI) for green and red wavelengths. What are the implications of the main findings? The accuracy of water quality data retrieved from satellite imagery can be improved by straightforward methods to pre-classify waterbodies into optical water types using data that can be calculated directly from satellite reflectance data. AVW is an appropriate metric to use in developing OWTs from multidimensional, satellite-derived data. For lakes in the region of study, OWTs can be derived using just two metrics, AVW and a metric of spectral magnitude, such as the trapezoidal area at red, green, and blue bands, ARGB, or similar metrics. Pre-classification of lakes into optical water types (OWTs) is considered a useful step in analyzing satellite-based reflectance data. We used a dataset of 109 reflectance hyperspectra from Minnesota and Wisconsin lakes and rivers to evaluate the usefulness of pre-classification to improve the retrieval of water quality information from satellite data. Three OWT classes were derived from the dataset by K-means clustering using three integrative metrics of reflectance spectral shape and magnitude as clustering variables. Values of the three metrics can be determined from satellite reflectance data as well as hyperspectral data. The OWT classes had distinct water quality characteristics in terms of Secchi depth, chlorophyll-a, and colored dissolved organic matter (CDOM). Algorithms used to retrieve values of the variables from simulated Sentinel-2 band reflectance data usually yielded more accurate predictions when computed separately for each class than when computed for the entire dataset, although exceptions were found for some fitting metrics and models and results for chlorophyll-a were not definitive. The three water quality variables were related in distinct ways to the integrative shape metric of reflectance spectra, apparent visible wavelength (AVW), supporting its use to develop OWTs to organize waterbodies into water quality classes. AVW was correlated (r = 0.933) with the integrative metric, normalized difference index at green and red wavelengths (NDI). Based on that result, we found that OWTs developed using just two variables, AVW and a metric of spectral magnitude, were nearly the same as classifications using all three integrative metrics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18111818
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        Text: English
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        PageCount: 26
        StartPage: 1818
    Subjects:
      – SubjectFull: Water quality
        Type: general
      – SubjectFull: Spectral reflectance
        Type: general
      – SubjectFull: Water quality monitoring
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Dissolved organic matter
        Type: general
    Titles:
      – TitleFull: Optical Water Types and Their Importance in Predicting Water Quality Metrics by Satellite Imagery.
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              Text: Jun2026
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              Y: 2026
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