Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data

Abstract

The goal of this study was to improve PhytoDOAS, which is a new retrieval method for quantitative identification of major phytoplankton functional types (PFTs) using hyper-spectral satellite data. PhytoDOAS is an extension of the Differential Optical Absorption Spectroscopy (DOAS, a method for detection of atmospheric trace gases), developed for remote identification of oceanic phytoplankton groups. Thus far, PhytoDOAS has been successfully exploited to identify cyanobacteria and diatoms over the global ocean from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) hyper-spectral data. This study aimed to improve PhytoDOAS for remote identification of coccolithophores, another functional group of phytoplankton. The main challenge for retrieving more PFTs by PhytoDOAS is to overcome the correlation effects between different PFT absorption spectra. Different PFTs are composed of different types and amounts of pigments, but also have pigments in common, e.g. chl <i>a</i>, causing correlation effects in the usual performance of the PhytoDOAS retrieval. Two ideas have been implemented to improve PhytoDOAS for the PFT retrieval of more phytoplankton groups. Firstly, using the fourth-derivative spectroscopy, the peak positions of the main pigment components in each absorption spectrum have been derived. After comparing the corresponding results of major PFTs, the optimized fit-window for the PhytoDOAS retrieval of each PFT was determined. Secondly, based on the results from derivative spectroscopy, a simultaneous fit of PhytoDOAS has been proposed and tested for a selected set of PFTs (coccolithophores, diatoms and dinoflagellates) within an optimized fit-window, proven by spectral orthogonality tests. The method was then applied to the processing of SCIAMACHY data over the year 2005. Comparisons of the PhytoDOAS coccolithophore retrievals in 2005 with other coccolithophore-related data showed similar patterns in their seasonal distributions, especially in the North Atlantic and the Arctic Sea. The seasonal patterns of the PhytoDOAS coccolithophores indicated very good agreement with the coccolithophore modeled data from the NASA Ocean Biochemical Model (NOBM), as well as with the global distributions of particulate inorganic carbon (PIC), provided by MODIS (MODerate resolution Imaging Spectroradiometer)-Aqua level-3 products. Moreover, regarding the fact that coccolithophores belong to the group of haptophytes, the PhytoDOAS seasonal coccolithophores showed good agreement with the global distribution of haptophytes, derived from synoptic pigment relationships applied to SeaWiFS chl <i>a</i>. As a case study, the simultaneous mode of PhytoDOAS has been applied to SCIAMACHY data for detecting a coccolithophore bloom which was consistent with the MODIS RGB image and the MODIS PIC map of the bloom, indicating the functionality of the method also in short-term retrievals

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

“…Phytoplankton composition product References ABUNDANCE Size classes Uitz et al, 2006;Brewin et al, 2010Brewin et al, , 2015 Size classes and multiple taxa Hirata et al, 2011 SPECTRAL REFLECTANCE Multiple taxa Alvain et al, 2005Alvain et al, , 2008Li et al, 2013;Ben Mustapha et al, 2014 Single taxon Coccolithophores Brown and Yoder, 1994;Moore et al, 2012Trichodesmium Subramaniam et al, 2002Westberry et al, 2005 ABSORPTION Size index Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012 Size classes Devred et al, 2006Devred et al, , 2011Hirata et al, 2008;Fujiwara et al, 2011;Roy et al, 2013 Multiple taxa Bracher et al, 2009;Sadeghi et al, 2012a;Werdell et al, 2014 BACK-SCATTERING Size classes Kostadinov et al, 2009Kostadinov et al, , 2016Fujiwara et al, 2011 ECOLOGICAL Taxonomic groups Palacz et al, 2013 Frontiers in Marine Science | www.frontiersin.orgFIGURE 1 | Illustration of phytoplankton diversity as found in nature impacted by environmental conditions, and how it can be derived from observations and modeling. Through in situ measurements (which represent the most real conditions), phytoplankton are grouped according to cellular traits that influence their optical properties such as pigments, size, morphology, and fluorescence, all also responding to photophysiology, which are named optical features of phytoplankton groups (PG).…”

“…by the variation in phytoplankton structure and pigment composition (Brown and Yoder, 1994;Subramaniam et al, 2002;Alvain et al, 2005Alvain et al, , 2008Westberry et al, 2005;Ciotti and Bricaud, 2006;Devred et al, 2006Devred et al, , 2011Hirata et al, 2008;Bracher et al, 2009;Kostadinov et al, 2009Kostadinov et al, , 2016Mouw and Yoder, 2010;Fujiwara et al, 2011;Bricaud et al, 2012;Moore et al, 2012;Sadeghi et al, 2012a;Li et al, 2013;Roy et al, 2013;Ben Mustapha et al, 2014;Werdell et al, 2014). Spectral-based approaches exploit as much of the backscattered spectrum observed by satellite as necessary to extract the signatures of specific PG to ocean color.…”

“…Products obtained from the PG algorithms (Table 2) are typically dominance (Brown and Yoder, 1994;Alvain et al, 2005;Moore et al, 2012;Ben Mustapha et al, 2014), presence or absence of a certain PT (Westberry et al, 2005;Werdell et al, 2014), fraction or concentration of chl-a of the three PSC (Devred et al, 2006(Devred et al, , 2011Uitz et al, 2006;Hirata et al, 2008Hirata et al, , 2011Kostadinov et al, 2009Kostadinov et al, , 2016Brewin et al, 2010Brewin et al, , 2015Fujiwara et al, 2011;Li et al, 2013;Roy et al, 2013) or a size factor characterizing the contribution of pico-(or micro-) phytoplankton to the phytoplankton community (Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012). Currently, only the products OC-PFT and PhytoDOAS (Bracher et al, 2009;Sadeghi et al, 2012a) enable the simultaneous determination of chl-a for several PT. PhytoDOAS retrieves the imprints of absorption characteristics of specific phytoplankton groups among all other atmospheric and oceanic absorbers from top of atmosphere data of the hyperspectral satellite sensor SCIAMACHY (Scanning Imaging Absorption Spectrometers for Atmospheric Chartography).…”

“…Inline (coupled) flow cytometry and microscopy techniques have been developed and enable a more precise classification of the phytoplankton groupings than HPLC marker pigments (e.g., Sosik and Olson, 2007). In addition, phytoplankton group specific Inherent Optical Properties (IOPs, i.e., absorption and backscattering) determined in the field have been used as algorithm inputs for several spectral approaches (Ciotti and Bricaud, 2006;Bracher et al, 2009;Mouw and Yoder, 2010;Fujiwara et al, 2011;Sadeghi et al, 2012a). Hyperspectral IOP measurements when obtained via continuous measurements (e.g., Boss et al, 2013) can help validating satellite-derived PG by increasing the number of match-ups, assessing variability within a satellite pixel, and quantifying the uncertainties in the two-step satellite methods (i.e., from water-leaving reflectance to IOP to PG).…”

Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

“…Some remote-sensing methods are able to detect phytoplankton size classes (e.g., Uitz et al, 2006;Mouw and Yoder, 2010;Brewin et al, 2011;Devred et al, 2011;Bricaud et al, 2012;Li et al, 2013 or particle size distribution, Kostadinov et al, 2009Kostadinov et al, , 2010, giving a global distribution of phytoplankton size in the ocean. In addition, several algorithms have been developed to identify: (i) one specific PFT from space (e.g., Smyth et al, 2002;Subramaniam et al, 2002;Sathyendranath et al, 2004) , and (ii) several PFTs (e.g., Aiken et al, 2007;Alvain et al, 2008;Raitsos et al, 2008;Bracher et al, 2009;Hirata et al, 2011;Sadeghi et al, 2012). A review of the different PFTs detection methods, based either on direct analysis of remote-sensing data or on empirical and semi-empirical approaches, can be found in recent papers (Nair et al, 2008;Brewin et al, 2011;Sathyendranath et al, 2014;Bracher et al, 2015b;Mouw et al, 2017).…”