An inverse modeling approach to estimating phytoplankton pigment concentrations from phytoplankton absorption spectra

Moisan, John R.; Moisan, Tiffany; Linkswiler, Matthew A.

doi:10.1029/2010jc006786

Cited by 55 publications

(64 citation statements)

References 32 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With knowledge of biomass concentration and distribution and the surrounding medium, an inverse modelling approach may allow pigment mass fractions to be estimated (e.g. Bigidare et al, 1990;Bricaud et al, 2004;Moisan et al, 2011), which could in turn enable inferences to be made about environmental stresses and stress responses in the ice surface algal ecosystem. The simulations shown in Fig.…”

Section: Characterising the Optical Properties Of Individual Cellsmentioning

confidence: 99%

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

et al. 2017

View full text Add to dashboard Cite

Abstract. The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo ("bioalbedo") and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.

show abstract

Section: Characterising the Optical Properties Of Individual Cellsmentioning

confidence: 99%

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Removal of atmospheric effects enables further analysis of the reflectance spectra to determine which constituents are present at any given time. While several approaches have been proposed to do this, spectral shape-based algorithms show promise at partitioning the variance associated with these complex optical mixtures (Simis et al, 2005;Moisan et al, 2011;Chase et al, 2013;Shuchman et al, 2013). Application of these methods in these complex environments suggests that they will be effective at less complex marine applications.…”

Section: Implications For Reflectance Estimation and Atmospheric Corrmentioning

confidence: 99%

Intercomparison of Approaches to the Empirical Line Method for Vicarious Hyperspectral Reflectance Calibration

et al. 2017

View full text Add to dashboard Cite

Analysis of visible remote sensing data research requires removing atmospheric effects by conversion from radiance to at-surface reflectance. This conversion can be achieved through theoretical radiative transfer models, which yield good results when well-constrained by field observations, although these measurements are often lacking. Additionally, radiative transfer models often perform poorly in marine or lacustrine settings or when complex air masses with variable aerosols are present. The empirical line method (ELM) measures reference targets of known reflectance in the scene. ELM methods require minimal environmental observations and are conceptually simple. However, calibration coefficients are unique to the image containing the reflectance reference. Here we compare the conversion of hyperspectral radiance observations obtained with the NASA Glenn Research Center Hyperspectral Imager to at-surface reflectance factor using two reflectance reference targets. The first target employs spherical convex mirrors, deployed on the water surface to reflect ambient direct solar and hemispherical sky irradiance to the sensor. We calculate the mirror gain using near concurrent at-sensor reflectance, integrated mirror radiance, and in situ water reflectance. The second target is the Lambertian-like blacktop surface at Maumee Bay State Park, Oregon, OH, where reflectance was measured concurrently by a downward looking, spectroradiometer on the ground, the aerial hyperspectral imager and an upward looking spectroradiometer on the aircraft. These methods allows us to produce an independently calibrated at-surface water reflectance spectrum, when atmospheric conditions are consistent. We compare the mirror and blacktop-corrected spectra to the in situ water reflectance, and find good agreement between methods. The blacktop method can be applied to all scenes, while the mirror calibration method, based on direct observation of the light illuminating the scene validates the results. The two methods are complementary and a powerful evaluation of the quality of atmospheric correction over extended areas. We decompose the resulting spectra using varimax-rotated, principal component analysis, yielding information about the underlying color producing agents that contribute to the Ortiz et al.Vicarious Hyperspectral Reflectance Calibration Methods observed reflectance factor scene, identifying several spectrally and spatially distinct mixtures of algae, cyanobacteria, illite, haematite, and goethite. These results have implications for future hyperspectral remote sensing missions, such as PACE, HyspIRI, and GeoCAPE.

show abstract

“…Low spectral resolution data has been used to estimate in-situ chlorophyll concentration from the peak height of the 676 nm Sorret band in an oligotrophic lake (Davis et al, 1997;Boss et al, 2007). High spectral resolution particulate absorption data were shown to provide concentrations of in-situ phytoplankton pigments and hence to extract information on the underlying phytoplankton population (Moisan et al, 2011;Chase et al, 2013).…”

Section: Combination Of Several Optical Properties Spectral Optical Pmentioning

confidence: 99%

Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES

Boss

Guidi

Richardson

et al. 2015

Progress in Oceanography

View full text Add to dashboard Cite

a b s t r a c tField and laboratory characterization of marine particles is laborious and expensive. Proxies of particle properties have been developed that allow researchers to obtain high frequency distributions of such properties in space or time. We focus on optical techniques used to characterize marine particles in-situ, with a focus on GEOTRACES-relevant properties, such as bulk properties including particle mass, crosssectional area, particle size distribution, particle shape information, and also single particle optical properties, such as individual particle type and size. We also address the use of optical properties of particles to infer particulate organic or inorganic carbon. In addition to optical sensors we review advances in imaging technology and its use to study marine particles in situ. This review addresses commercially available technology and techniques that can be used as a proxy for particle properties and the associated uncertainties with particular focus to open ocean environments, the focus of GEOTRACES.

show abstract

An inverse modeling approach to estimating phytoplankton pigment concentrations from phytoplankton absorption spectra

Cited by 55 publications

References 32 publications

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

Intercomparison of Approaches to the Empirical Line Method for Vicarious Hyperspectral Reflectance Calibration

Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES

Contact Info

Product

Resources

About