A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements
Accurate estimates of the oceanic particulate organic carbon concentration (POC) from optical measurements have remained challenging because interactions between light and natural assemblages of marine particles are complex, depending on particle concentration, composition, and size distribution. In...
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.941950/full |
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| author | Daniel Koestner Daniel Koestner Dariusz Stramski Rick A. Reynolds |
| author_facet | Daniel Koestner Daniel Koestner Dariusz Stramski Rick A. Reynolds |
| author_sort | Daniel Koestner |
| collection | DOAJ |
| description | Accurate estimates of the oceanic particulate organic carbon concentration (POC) from optical measurements have remained challenging because interactions between light and natural assemblages of marine particles are complex, depending on particle concentration, composition, and size distribution. In particular, the applicability of a single relationship between POC and the spectral particulate backscattering coefficient bbp(λ) across diverse oceanic environments is subject to high uncertainties because of the variable nature of particulate assemblages. These relationships have nevertheless been widely used to estimate oceanic POC using, for example, in situ measurements of bbp from Biogeochemical (BGC)-Argo floats. Despite these challenges, such an in situbased approach to estimate POC remains scientifically attractive in view of the expanding global-scale observations with the BGC-Argo array of profiling floats equipped with optical sensors. In the current study, we describe an improved empirical approach to estimate POC which takes advantage of simultaneous measurements of bbp and chlorophyll-a fluorescence to better account for the effects of variable particle composition on the relationship between POC and bbp. We formulated multivariable regression models using a dataset of field measurements of POC, bbp, and chlorophyll-a concentration (Chla), including surface and subsurface water samples from the Atlantic, Pacific, Arctic, and Southern Oceans. The analysis of this dataset of diverse seawater samples demonstrates that the use of bbp and an additional independent variable related to particle composition involving both bbp and Chla leads to notable improvements in POC estimations compared with a typical univariate regression model based on bbp alone. These multivariable algorithms are expected to be particularly useful for estimating POC with measurements from autonomous BGC-Argo floats operating in diverse oceanic environments. We demonstrate example results from the multivariable algorithm applied to depth-resolved vertical measurements from BGC-Argo floats surveying the Labrador Sea. |
| first_indexed | 2024-04-14T03:09:53Z |
| format | Article |
| id | doaj.art-e10b0ecfe0df4a978f640308933c8c72 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-14T03:09:53Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-e10b0ecfe0df4a978f640308933c8c722022-12-22T02:15:38ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-08-01910.3389/fmars.2022.941950941950A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a MeasurementsDaniel Koestner0Daniel Koestner1Dariusz Stramski2Rick A. Reynolds3Remote Sensing Division, US Naval Research Laboratory, Washington, DC, United StatesDepartment of Physics and Technology, University of Bergen, Bergen, NorwayMarine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United StatesMarine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United StatesAccurate estimates of the oceanic particulate organic carbon concentration (POC) from optical measurements have remained challenging because interactions between light and natural assemblages of marine particles are complex, depending on particle concentration, composition, and size distribution. In particular, the applicability of a single relationship between POC and the spectral particulate backscattering coefficient bbp(λ) across diverse oceanic environments is subject to high uncertainties because of the variable nature of particulate assemblages. These relationships have nevertheless been widely used to estimate oceanic POC using, for example, in situ measurements of bbp from Biogeochemical (BGC)-Argo floats. Despite these challenges, such an in situbased approach to estimate POC remains scientifically attractive in view of the expanding global-scale observations with the BGC-Argo array of profiling floats equipped with optical sensors. In the current study, we describe an improved empirical approach to estimate POC which takes advantage of simultaneous measurements of bbp and chlorophyll-a fluorescence to better account for the effects of variable particle composition on the relationship between POC and bbp. We formulated multivariable regression models using a dataset of field measurements of POC, bbp, and chlorophyll-a concentration (Chla), including surface and subsurface water samples from the Atlantic, Pacific, Arctic, and Southern Oceans. The analysis of this dataset of diverse seawater samples demonstrates that the use of bbp and an additional independent variable related to particle composition involving both bbp and Chla leads to notable improvements in POC estimations compared with a typical univariate regression model based on bbp alone. These multivariable algorithms are expected to be particularly useful for estimating POC with measurements from autonomous BGC-Argo floats operating in diverse oceanic environments. We demonstrate example results from the multivariable algorithm applied to depth-resolved vertical measurements from BGC-Argo floats surveying the Labrador Sea.https://www.frontiersin.org/articles/10.3389/fmars.2022.941950/fullmarine opticsparticulate organic carboninherent optical propertiesbackscattering coefficientchlorophyll-a fluorescencebiogeochemical-argo observations |
| spellingShingle | Daniel Koestner Daniel Koestner Dariusz Stramski Rick A. Reynolds A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements Frontiers in Marine Science marine optics particulate organic carbon inherent optical properties backscattering coefficient chlorophyll-a fluorescence biogeochemical-argo observations |
| title | A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements |
| title_full | A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements |
| title_fullStr | A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements |
| title_full_unstemmed | A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements |
| title_short | A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements |
| title_sort | multivariable empirical algorithm for estimating particulate organic carbon concentration in marine environments from optical backscattering and chlorophyll a measurements |
| topic | marine optics particulate organic carbon inherent optical properties backscattering coefficient chlorophyll-a fluorescence biogeochemical-argo observations |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.941950/full |
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