Remote sensing of chlorophyll-a in coastal waters based on the light absorption coefficient of phytoplankton
Remote sensing of chlorophyll-a concentration, [Chl-a], has been difficult in coastal waters like the Chesapeake Bay owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not covary with phytoplankton. Here we revisit the semi-analytical pathway o...
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Published in | Remote sensing of environment Vol. 201; pp. 331 - 341 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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01.11.2017
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Abstract | Remote sensing of chlorophyll-a concentration, [Chl-a], has been difficult in coastal waters like the Chesapeake Bay owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not covary with phytoplankton. Here we revisit the semi-analytical pathway of deriving [Chl-a] based on the light absorption coefficient of phytoplankton by introducing the generalized stacked-constraints model (GSCM) to partition satellite-derived total light absorption coefficient of water (with pure-water contribution subtracted), anw(λ), into phytoplankton, aph(λ), and non-phytoplankton components, where anw(λ) is derived from satellite remote-sensing reflectance, Rrs(λ), using the Quasi-Analytical Algorithm. The GSCM-derived aph(λ) was compared with field matchups of [Chl-a]. We show that semi-analytical approaches can provide superior [Chl-a] product compared with reflectance-band-ratio algorithms when the accuracy of satellite-derived aph(λ) is sufficiently improved, in this case with the GSCM. However, the improvement is at the cost of significantly reduced data availability because the GSCM may provide no feasible solutions when input anw(λ) data are subject to large errors. This in turn highlights the needs for improved atmospheric correction and upstream models capable of preserving actual spectral shapes of Rrs(λ) and anw(λ), respectively.
•Revisited the pathway to derive chlorophyll from light absorption coefficient.•Applied a new model to eliminate the interference of terrestrial materials.•Provided improved chlorophyll product decoupled from nonalgal optical properties. |
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AbstractList | Remote sensing of chlorophyll-a concentration, [Chl-a], has been difficult in coastal waters like the Chesapeake Bay owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not covary with phytoplankton. Here we revisit the semi-analytical pathway of deriving [Chl-a] based on the light absorption coefficient of phytoplankton by introducing the generalized stacked-constraints model (GSCM) to partition satellite-derived total light absorption coefficient of water (with pure-water contribution subtracted), anw(λ), into phytoplankton, aph(λ), and non-phytoplankton components, where anw(λ) is derived from satellite remote-sensing reflectance, Rrs(λ), using the Quasi-Analytical Algorithm. The GSCM-derived aph(λ) was compared with field matchups of [Chl-a]. We show that semi-analytical approaches can provide superior [Chl-a] product compared with reflectance-band-ratio algorithms when the accuracy of satellite-derived aph(λ) is sufficiently improved, in this case with the GSCM. However, the improvement is at the cost of significantly reduced data availability because the GSCM may provide no feasible solutions when input anw(λ) data are subject to large errors. This in turn highlights the needs for improved atmospheric correction and upstream models capable of preserving actual spectral shapes of Rrs(λ) and anw(λ), respectively. Remote sensing of chlorophyll-a concentration, [Chl-a], has been difficult in coastal waters like the Chesapeake Bay owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not covary with phytoplankton. Here we revisit the semi-analytical pathway of deriving [Chl-a] based on the light absorption coefficient of phytoplankton by introducing the generalized stacked-constraints model (GSCM) to partition satellite-derived total light absorption coefficient of water (with pure-water contribution subtracted), anw(λ), into phytoplankton, aph(λ), and non-phytoplankton components, where anw(λ) is derived from satellite remote-sensing reflectance, Rrs(λ), using the Quasi-Analytical Algorithm. The GSCM-derived aph(λ) was compared with field matchups of [Chl-a]. We show that semi-analytical approaches can provide superior [Chl-a] product compared with reflectance-band-ratio algorithms when the accuracy of satellite-derived aph(λ) is sufficiently improved, in this case with the GSCM. However, the improvement is at the cost of significantly reduced data availability because the GSCM may provide no feasible solutions when input anw(λ) data are subject to large errors. This in turn highlights the needs for improved atmospheric correction and upstream models capable of preserving actual spectral shapes of Rrs(λ) and anw(λ), respectively. •Revisited the pathway to derive chlorophyll from light absorption coefficient.•Applied a new model to eliminate the interference of terrestrial materials.•Provided improved chlorophyll product decoupled from nonalgal optical properties. |
Author | Zheng, Guangming DiGiacomo, Paul M. |
Author_xml | – sequence: 1 givenname: Guangming orcidid: 0000-0003-4624-7976 surname: Zheng fullname: Zheng, Guangming email: guangming.zheng@noaa.gov organization: NOAA/NESDIS Center for Satellite Application and Research, 5830 University Research Court, College Park, MD 20740, USA – sequence: 2 givenname: Paul M. surname: DiGiacomo fullname: DiGiacomo, Paul M. organization: NOAA/NESDIS Center for Satellite Application and Research, 5830 University Research Court, College Park, MD 20740, USA |
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Keywords | Turbid coastal water Chlorophyll Partitioning Light absorption coefficient Stacked-constraints approach |
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SubjectTerms | Absorption Absorptivity Algorithms Atmospheric correction Atmospheric models Chesapeake Bay Chlorophyll Coastal environments coastal water Coastal waters Coefficients Constraint modelling Data reduction Decomposing organic matter Detection Electromagnetic absorption Humus Light absorption Light absorption coefficient Minerals Partitioning Phytoplankton Plankton Reflectance Remote sensing Satellites Stacked-constraints approach Turbid coastal water |
Title | Remote sensing of chlorophyll-a in coastal waters based on the light absorption coefficient of phytoplankton |
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