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Using Empirical Orthogonal Functions Derived from Remote-sensing Reflectance for the Prediction of Phytoplankton Pigment Concentrations : Volume 11, Issue 1 (03/02/2015)

By Bracher, A.

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Book Id: WPLBN0004020244
Format Type: PDF Article :
File Size: Pages 20
Reproduction Date: 2015

Title: Using Empirical Orthogonal Functions Derived from Remote-sensing Reflectance for the Prediction of Phytoplankton Pigment Concentrations : Volume 11, Issue 1 (03/02/2015)  
Author: Bracher, A.
Volume: Vol. 11, Issue 1
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Dinter, T., Taylor, M. H., Steinmetz, F., Taylor, B., Bracher, A., & Röttgers, R. (2015). Using Empirical Orthogonal Functions Derived from Remote-sensing Reflectance for the Prediction of Phytoplankton Pigment Concentrations : Volume 11, Issue 1 (03/02/2015). Retrieved from http://community.worldlibrary.net/


Description
Description: Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bussestraße 24, 27570 Bremerhaven, Germany. The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer – MERIS – Polymer product developed by Steinmetz et al., 2011) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.

Summary
Using empirical orthogonal functions derived from remote-sensing reflectance for the prediction of phytoplankton pigment concentrations

Excerpt
Bracher, A., Taylor, M. H., Taylor, B., Dinter, T., Röttgers, R., and Steinmetz, F.: Using empirical orthogonal functions derived from remote sensing reflectance for the prediction of concentrations of phytoplankton pigments, Ocean Sci. Discuss., 11, 2073–2117, doi:10.5194/osd-11-2073-2014, 2014.; Brewin, R. J. W., Sathyendranath, S., Müeller, D., Brockmann, C., Deschamps, P.-Y., Devred, E., Doerffer, R., Fomferra, N., Franz, B. A., Grant, M. G., Groom, S. B., Horseman, A., Hu C., Krasemann, H., Lee, Z., Maritorena, S., Mélin, F., Peters, M., Platt, T., Regner, P., Smyth, T., Steinmetz, F., Swinton, J., Werdell, J., and White III, G. N.: The Ocean Colour Climate Change Initiative: A round-robin comparison on in-water bio-optical algorithms, Remote Sens. Environ., in press, 2015.; Chase, A., Boss, E., Zaneveld, R., Bricaud, A., Claustre, H., Ras, J., Dall'Olmo, G., and Westberry, T. K.: Decomposition of in situ particulate absorption spectra, Methods Oceanogr., 7, 110–124, 2013.; Craig, S. E., Jones, C. T., Li, W. K. W., Lazin, G., Horne, E., Caverhill, C., and Cullen, J. J.: Deriving optical metrics of ecological variability from measurements of coastal ocean colour, Remote Sens. Environ., 119, 72–83, 2012.; Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., Isada, T., Howell, E., Hashioka, T., Noguchi-Aita, M., and Yamanaka, Y.: Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types, Biogeosciences, 8, 311–327, doi:10.5194/bg-8-311-2011, 2011.; Hooker, S. B., Van Heukelem, L., Thomas, C. S., Claustre, H., Ras, J., and Barlow, R.: The second SeaWiFS HPLC analysis round robin experiment (SeaHARRE-2). NASA TM/2005-212785, NASA Goddard Space Flight Center, Greenbelt, Maryland, 2005.; Lee, Z. P., Carder, K. L, and Arnone, R. A.: Deriving inherent optical properties from water color: A multi-band quasi-analytical algorithm for optically deep waters, Appl. Opt., 41, 5755–5772, 2002.; Longhurst, A.: Ecological Geography of the Sea, 2nd Edn., Elsevier Academic press, USA, 2006.; Letelier, R. M., Bidigare, R. R., Hebel, D. V., Ondrusek, M., Winn, C. D., and Karl, D. M.: Temporal variability of phytoplankton community structure based on pigment analysis. Limnol. Oceanogr., 38, 1420–1437, 1993.; Lubac, B. and Loisel, H.: Variability and classi?cation of remote sensing reflectance spectra in the eastern English Channel and southern North Sea, Remote Sens. Environ., 110, 45–48, 2007.; McClain, C. R.: A Decade of Satellite Ocean Color Observations, Ann. Rev. Marine Sci., 1, 19–42, doi:10.1146/annurev.marine.010908.163650, 2009.; Müller, D. and Krasemann, H.: Product validation and algorithm selection report, part 1 – atmospheric correction. Tech. Rep. AO-1/6207/09/I-LG D2.5, European Space Agency, ESRIN, 2012.; O'Reilly, J. E., Maritorena, S., Siegel, D., and O'Brien, M. C.: Ocean color chlorophyll a algorithms for SeaWiFs, OC2, and OC4: Technical report, edited by: Toole, D., Mitchell, B. G., Kahru, M., Chavez, F. P., Strutton, P., Cota, G., Hooker, S. B., McClain, C. R., Carder, K. L., Muller-Karger, F., Harding, L., Magnuson, A., Phinney, D., Moore, G. F., Aiken, J., Arrigo, K. R., Letelier, R., Culver, M., Hooker, S. B., and Firestone, E. R.: SeaWiFS postlaunch calibration and validation analyses, Part 3. NASA, Goddard Space Flight Center, Greenbelt, Maryland. SeaWiFS Postlaunch Technical Report Series, 11, 9–23, 2000.; Organelli, E., Bricaud, A., Antoine D., and Uitz, J.: Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site), Appl. Opt., 52, 2257–2273, 2013.; Aiken, J., Pradhan, Y., Barlow, R., Lavender, S., Po

 

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