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 inRemote sensing of environment Vol. 201; pp. 331 - 341
Main Authors Zheng, Guangming, DiGiacomo, Paul M.
Format Journal Article
LanguageEnglish
Published New York Elsevier Inc 01.11.2017
Elsevier BV
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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.
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.
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Keywords Turbid coastal water
Chlorophyll
Partitioning
Light absorption coefficient
Stacked-constraints approach
Language English
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Snippet Remote sensing of chlorophyll-a concentration, [Chl-a], has been difficult in coastal waters like the Chesapeake Bay owing largely to terrestrial substances...
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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
URI https://dx.doi.org/10.1016/j.rse.2017.09.008
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https://www.proquest.com/docview/2000577700
Volume 201
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