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

Using empirical orthogonal functions derived from remote sensing reflectance for the prediction of concentrations of phytoplankton pigments.

The composition and abundance of algal pigments provide information on characteristics of a phytoplankton community in respect to its photoacclimation, overall biomass, and taxonomic composition. Particularly, these 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 to filtered water samples. This method, like others when water samples have to be analysed in the laboratory, is time consuming and therefore only a limited number of data points can be obtained. 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 hyper-spectral underwater radiometric and from multispectral satellite data (using the MERIS Polymer product developed by Steinmetz et al., 2011) measured in the Eastern Tropical Atlantic. Subsequently we developed statistically linear 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 multi-spectral 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. The fitted statistical model constructed on the satellite reflectance data as input was applied to one 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 photo-physiology

Radiative transfer modeling through terrestrial atmosphere and ocean accounting for inelastic scattering processes: Software package SCIATRAN.

SCIATRAN is a comprehensive software package which is designed to model radiative transfer processes in the terrestrial atmosphere and ocean in the spectral range from the ultraviolet to the thermal infrared (0.18–40 μm). It accounts for multiple scattering processes, polarization, thermal emission and ocean–atmosphere coupling. The main goal of this paper is to present a recently developed version of SCIATRAN which takes into account accurately inelastic radiative processes in both the atmosphere and the ocean. In the scalar version of the coupled ocean–atmosphere radiative transfer solver presented by Rozanov et al. [61] we have implemented the simulation of the rotational Raman scattering, vibrational Raman scattering, chlorophyll and colored dissolved organic matter fluorescence. In this paper we discuss and explain the numerical methods used in SCIATRAN to solve the scalar radiative transfer equation including trans-spectral processes, and demonstrate how some selected radiative transfer problems are solved using the SCIATRAN package. In addition we present selected comparisons of SCIATRAN simulations with those published benchmark results, independent radiative transfer models, and various measurements from satellite, ground-based, and ship-borne instruments. The extended SCIATRAN software package along with a detailed User's Guide is made available for scientists and students, who are undertaking their own research typically at universities, via the web page of the Institute of Environmental Physics (IUP), University of Bremen: http://www.iup.physik.uni-bremen.de

High resolution water column phytoplankton composition across the Atlantic Ocean from ship-towed vertical undulating radiometry.

Different phytoplankton groups dominate ocean biomes and they drive differently the marine food web and the biogeochemical cycles. However, their distribution over most parts of the global ocean remains uncertain due to limitations in the sampling resolution of currently available in situ and satellite data. Information below surface waters are especially limited because satellite sensors only provide information on the first optical depth. We present measurements obtained during Polarstern cruise PS113 (May–June 2018) across the Atlantic Ocean from South America to Europe along numerous transects. We measured the hyperspectral underwater radiation field continuously over several hours from a vertical undulating platform towed behind the ship. Equivalent measurements were also taken at specific stations. The concentrations of phytoplankton pigments were determined on discrete water samples. Via diagnostic pigment analysis we derived the phytoplankton group chlorophyll a concentration (Chla) from this pigment data set. We obtained high resolution phytoplankton group Chla data from depth resolved apparent optical properties derived from the underwater radiation data by applying an empirical orthogonal function (EOF) analysis to the spectral data set and subsequently developing regression models using the pigment based phytoplankton group Chla and selected EOF modes. To our knowledge, this is the first data set with high horizontal coverage (50–150 km) and resolution (∼1 km) that is also resolved vertically for the Chla of major taxonomic phytoplankton groups. Subsampling with 500 permutations for cross validation verified the high robustness of our estimates to enable predictions of seven different phytoplankton groups’ Chla and of total Chla (R2 and median percent differences of the cross validation are within 0.45–0.68 and 29–53%, respectively). Our depth resolved phytoplankton groups’ Chla data reflect well the different biogeochemical provinces within the Atlantic Ocean transect and follow the distributions encountered by previous point observations. This verifies the high quality of our retrievals and provides the prospect to put similar radiometers on profiling floats or gliders which would enable the large-scale collection of vertically resolved phytoplankton data at much improved horizontal coverage relative to discrete sampling

Underway spectrophotometry in the Fram Strait (European Arctic Ocean): a highly resolved chlorophyll a data source for complementing satellite ocean color

Satellite remote sensing of chlorophyll a concentration (Chl-a) in the Arctic Ocean is spatially and temporally limited and needs to be supplemented and validated with substantial volumes of in situ observations. Here, we evaluated the capability of obtaining highly resolved in situ surface Chl-a using underway spectrophotometry operated during two summer cruises in 2015 and 2016 in the Fram Strait. Results showed that Chl-a measured using high pressure liquid chromatography (HPLC) was well related (R2 = 0.90) to the collocated particulate absorption line height at 676 nm obtained from the underway spectrophotometry system. This enabled continuous surface Chl-a estimation along the cruise tracks. When used to validate Chl-a operational products as well as to assess the Chl-a algorithms of the aqua moderate resolution imaging spectroradiometer (MODIS-A) and Sentinel-3 Ocean Land Color Imager (OLCI) Level 2 Chl-a operational products, and from OLCI Level 2 products processed with Polymer atmospheric correction algorithm (version 4.1), the underway spectrophotometry based Chl-a data sets proved to be a much more sufficient data source by generating over one order of magnitude more match-ups than those obtained from discrete water samples. Overall, the band ratio (OCI, OC4) Chl-a operational products from MODIS-A and OLCI as well as OLCI C2RCC products showed acceptable results. The OLCI Polymer standard output provided the most reliable Chl-a estimates, and nearly as good results were obtained from the OCI algorithm with Polymer atmospheric correction method. This work confirms the great advantage of the underway spectrophotometry in enlarging in situ Chl-a data sets for the Fram Strait and improving satellite Chl-a validation and Chl-a algorithm assessment over discrete water sample analysis in the laboratory

Investigation on heat transfer in the Laptev Sea with respect to regional climate changes

The Laptev Sea around the Lena River delta in northern Siberia is a very remote area that in-situ measurements are only sparsely available. Polar night and long lasting ice coverage until the end of June makes it difficult to investigate the area all year round. Here satellite investigations of radiances measured e.g. with Envisat MERIS satellite and derived inherent optical properties (IOP) may help to generate a time series of changing water constituents, e.g. chlorophyll and coloured organic matter which can be split further into coloured dissolved organic matter (CDOM) and suspended particles (SPM). However, large solar zenith angles as well as thin cloud coverage in summer after ice break-off makes it difficult to investigate this region by remote sensing applications. Therefore modelling approaches seems to be a useful first approximation to identify the feedback to the radiation budget in these remote areas. With the current studies we investigate the influence of CDOM and SPM on the radiative heat transfer into the shelf regions of the Laptev Sea. As a first step we use the coupled atmosphere-ocean radiative transfer model SCIATRAN to assess energy input into coastal waters of this region dependent on different concentrations of CDOM varying significantly for different times of the year. Low solar elevations and high absorption by water constituents in this area extremely reduces the light penetration depth in the water body. An increased absorption in the surface water leads to higher sea surface temperatures and a high energy release into the atmosphere often occurring in late autumn and consequently influences the ice development process. In the context of climate change and thawing permafrost in Siberia the riverine input of those highly absorbing particles by Lena river may increase in the future. Therefore, a better understanding of these processes is necessary to predict possible future changes for that remote area