Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data

Abstract

Abstract. In this study the technique of Differential OpticalAbsorption Spectroscopy (DOAS) has been adapted for the retrieval of the absorption and biomass of two major phytoplankton groups (PhytoDOAS) from data of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite sensor. SCIAMACHY measures back scattered solar radiation in the UV-Vis-NIR spectral regions with a high spectral resolution (0.2 to 1.5 nm). In order to identify phytoplankton absorption characteristics in the SCIAMACHY data in the range of 430 to 500 nm, phytoplankton absorption spectra measured in-situ during two different RV "Polarstern" expeditions were used. The two spectra have been measured in different ocean regions where different phytoplankton groups (cyanobacteria and diatoms) dominated the phytoplankton composition. Results clearly show distinct absorption characteristics of the two phytoplankton groups in the SCIAMACHY spectra. Using these results in addition to calculations of the light penetration depth derived from DOAS retrievals of the inelastic scattering (developed by Vountas et al., 2007), globally distributed pigment concentrations for these characteristic phytoplankton groups for two monthly periods (FebruaryMarch 2004 and October-November 2005) were determined. This satellite information on cyanobacteria and diatoms distribution clearly matches the concentrations based on high pressure liquid chromatography (HPLC) pigment analysis of collocated water samples and concentrations derived from a Correspondence to: A. Bracher (astrid.bracher@awi.de) global model analysis with the NASA Ocean Biogeochemical Model (Gregg et al., 2003;Gregg and Casey 2007). The quantitative assessment of the distribution of key phytoplankton groups from space enables various biogeochemical regions to be distinguished and will be of great importance for the global modeling of marine ecosystems and biogeochemical cycles which enables the impact of climate change in the oceanic biosphere to be estimated.

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

“…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).…”

“…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).…”

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

“…Although non-quantitative, these observations would be useful to estimate the contribution of assemblages to the chlorophyll-a estimation (Alvain et al, 2006 and then completing approaches such as OC-PFT , PhytoDAS (Bracher et al, 2009). In this way, we support that development of in-situ measurements will allow us to identify more phytoplankton assemblages and better understand the limitation of this empirical approach.…”

“…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).…”