Remote sensing of coccolithophore blooms in selected oceanic regions using the PhytoDOAS method applied to hyper-spectral satellite data

In this study temporal variations of coccolithophore blooms are investigated using satellite data. Eight years (from 2003 to 2010) of data of SCIAMACHY, a hyper-spectral satellite sensor on-board ENVISAT, were processed by the PhytoDOAS method to monitor the biomass of coccolithophores in three selected regions. These regions are characterized by frequent occurrence of large coccolithophore blooms. The retrieval results, shown as monthly mean time series, were compared to related satellite products, including the total surface phytoplankton, i.e. total chlorophyll a (from GlobColour merged data) and the particulate inorganic carbon (from MODIS-Aqua). The inter-annual variations of the phytoplankton bloom cycles and their maximum monthly mean values have been compared in the three selected regions to the variations of the geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind-speed, which are known to affect phytoplankton dynamics. For each region, the anomalies and linear trends of the monitored parameters over the period of this study have been computed. The patterns of total phytoplankton biomass and specific dynamics of coccolithophore chlorophyll a in the selected regions are discussed in relation to other studies. The PhytoDOAS results are consistent with the two other ocean color products and support the reported dependencies of coccolithophore biomass dynamics on the compared geophysical variables. This suggests that PhytoDOAS is a valid method for retrieving coccolithophore biomass and for monitoring its bloom developments in the global oceans. Future applications of time series studies using the PhytoDOAS data set are proposed, also using the new upcoming generations of hyper-spectral satellite sensors with improved spatial resolution

Cloud and surface classification using SCIAMACHY polarization measurement devices

International audienceA simple scheme has been developed to discriminate surface, sun glint and cloud properties in satellite based spectrometer data utilizing visible and near infrared information. It has been designed for the use with data measured by SCIAMACHY's (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY) Polarization Measurement Devices but the applicability is not strictly limited to this instrument. The scheme is governed by a set of constraints and thresholds developed by using satellite imagery and meteorological data. Classification targets are ice, water and generic clouds, sun glint and surface parameters, such as water, snow/ice, desert and vegetation. The validation is done using MERIS (MEdium Resolution Imaging Spectrometer) and meteorological data from METAR (MÉTéorologique Aviation Régulière ? a network for the provision of meteorological data for aviation). Qualitative and quantitative validation using MERIS satellite imagery shows good agreement. The comparison with METAR data exhibits very good agreement

Spectral studies of ocean water using DOAS

International audienceMethods enabling the retrieval of oceanic parameter from the space borne instrumentation Scanning Imaging Absorption Spectrometer for Atmospheric ChartographY (SCIAMACHY) using Differential Optical Absorption Spectroscopy (DOAS) are presented. SCIAMACHY onboard ENVISAT measures back scattered solar radiation at a spectral resolution (0.2 to 1.5 nm). The DOAS method was used for the first time to fit modelled Vibrational Raman Scattering (VRS) in liquid water and in situ measured phytoplankton absorption reference spectra to optical depths measured by SCIAMACHY. Spectral structures of VRS and phytoplankton absorption were clearly found in these optical depths. Both fitting approaches lead to consistent results. DOAS fits correlate with estimates of chlorophyll concentrations: low fit factors for VRS retrievals correspond to large chlorophyll concentrations and vice versa; large fit factors for phytoplankton absorption correspond with high chlorophyll concentrations and vice versa. From these results a simple retrieval technique taking advantage of both measurements is shown. First maps of global chlorophyll concentrations were compared to the corresponding MODIS measurements with very promising results. In addition, results from this study will be used to improve atmospheric trace gas DOAS-retrievals from visible wavelengths by including these oceanographic signatures

Corrigendum to "Spectral studies of ocean water with space-borne sensor SCIAMACHY using Differential Optical Absorption Spectroscopy (DOAS)" published in Ocean Sci., 3, 429–440, 2007

China: Red ChinaForman, H. (1946). Report from Red China. London: Robert Hale Limited.GrayscaleForman Nitrate Negatives, Box 1

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

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

Corrigendum: Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT) [Front. Mar. Sci,(203),4] DOI: 10.3389/fmars.2017.00203

This is the final version. Available on open access from Frontiers Media via the DOI in this recordThe article to which this is the corrigendum is in ORE at http://hdl.handle.net/10871/38250In the original article, we neglected, but would like to acknowledge the North-German Supercomputing Alliance (HLRN) for providing HPC resources that have contributed to the research results reported in this paper. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way

Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT)

This is the final version. Available from Frontiers Media via the DOI in this record.The corrigendum to this article is in ORE at http://hdl.handle.net/10871/38256We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) - diatoms, coccolithophores and cyanobacteria - by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.ESA SEOM SY-4Sci Synergy projectSFB/TR 172 (AC)3 “Arctic Amplification” subproject C03DFG-Priority Program SPP 1158 “Antarktis” PhySyn BU2913/3-1Helmholtz Climate Initiative REKLIMHelmholtz Association of German Research Centres (HGF