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

Inelastic scattering in ocean water and its impact on trace gas retrievals from satellite data

Over clear ocean waters, photons scattered within the water body contribute significantly to the upwelling flux. In addition to elastic scattering, inelastic Vibrational Raman Scattering (VRS) by liquid water is also playing a role and can have a strong impact on the spectral distribution of the outgoing radiance. Under clear-sky conditions, VRS has an influence on trace gas retrievals from space-borne measurements of the backscattered radiance such as from e.g. GOME (Global Ozone Monitoring Experiment). The effect is particularly important for geo-locations with small solar zenith angles and over waters with low chlorophyll concentration.<br> <br> In this study, a simple ocean reflectance model (Sathyendranath and Platt, 1998) accounting for VRS has been incorporated into a radiative transfer model. The model has been validated by comparison with measurements from a swimming-pool experiment dedicated to detect the effect of scattering within water on the outgoing radiation and also with selected data sets from GOME. The comparisons show good agreement between experimental and model data and highlight the important role of VRS.<br> <br> To evaluate the impact of VRS on trace gas retrieval, a sensitivity study was performed on synthetic data. If VRS is neglected in the data analysis, errors of more than 30% are introduced for the slant column (<i>SC</i>) of BrO over clear ocean scenarios. Exemplarily DOAS retrievals of BrO from real GOME measurements including and excluding a VRS compensation led to comparable results as in the sensitivity study, but with somewhat smaller differences between the two analyses.<br> <br> The results of this work suggest, that DOAS retrievals of atmospheric trace species from measurements of nadir viewing space-borne instruments have to take VRS scattering into account over waters with low chlorophyll concentrations, and that a simple correction term is enough to reduce the errors to an acceptable level

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

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

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

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

International audienceNo abstract available

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

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 &lt;i&gt;a&lt;/i&gt;, 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 &lt;i&gt;a&lt;/i&gt;. 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

The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study

The eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm has been designed for the top-of-atmosphere reflectance measured by the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3 to derive snow properties: snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) under cloud-free conditions. This is the first part of the paper, to describe the retrieval method and the sensitivity study. Nine pre-defined SPSs (aggregate of 8 columns, droxtal, hollow bullet rosette, hollow column, plate, aggregate of 5 plates, aggregate of 10 plates, solid bullet rosette, column) are used to describe the snow optical properties. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach. The SSA is then calculated using another pre-calculated LUT for the retrieved SGS and SPS. The optical properties (e.g., phase function) of the ice crystals can reproduce the wavelength-dependent and angular-dependent snow reflectance features, compared to laboratory measurements. A comprehensive study to understand the impact of aerosols, SPS, ice crystal surface roughness, cloud contamination, instrument spectral response function, the snow habit mixture model and snow vertical inhomogeneity in the retrieval accuracy of snow properties has been performed based on SCIATRAN radiative transfer simulations. The main findings are (1) snow angular and spectral reflectance features can be described by the predefined ice crystal properties only when both SGS and SPS can be optimally and iteratively obtained; (2) the impact of ice crystal surface roughness on the retrieval results is minor; (3) SGS and SSA show an inverse linear relationship; (4) the retrieval of SSA assuming a non-convex particle shape, compared to a convex particle shape (e.g., sphere), shows larger retrieval results; (5) aerosol/cloud contamination due to unperfected atmospheric correction and cloud screening introduces underestimation of SGS, “inaccurate” SPS and overestimation of SSA; (6) the impact of the instrument spectral response function introduces an overestimation into retrieved SGS, introduces an underestimation into retrieved SSA and has no impact on retrieved SPS; and (7) the investigation, by taking an ice crystal particle size distribution and habit mixture into account, reveals that XBAER-retrieved SGS agrees better with the mean size, rather than with the mode size, for a given particle size distribution.</p

A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements

The accurate identification of the presence of cloud in the ground scenes observed by remote-sensing satellites is an end in itself. The lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of aerosol optical thickness (AOT) is the knowledge of the presence of cloud in a ground scene. In our study, observations of the upwelling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared (TIR), coupled with solar extraterrestrial irradiance, are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the reflectance observations of the Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A and -3B. The resultant AATSR–SLSTR cloud identification algorithm (ASCIA) addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloud-free surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson correlation coefficient (PCC) parameter is used to measure the “stability” of the atmosphere–surface system observed by satellites. The cloud-free surface is classified by analysing the PCC values on the block scale 25×25&thinsp;km2. Subsequently, the reflection at 3.7&thinsp;µm is used for accurate cloud identification at scene level: with areas of either 1×1 or 0.5×0.5&thinsp;km2. The ASCIA data product has been validated by comparison with independent observations, e.g. surface synoptic observations (SYNOP), the data from AErosol RObotic NETwork (AERONET) and the following satellite products: (i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the product from a method based on a clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate Resolution Imaging Spectroradiometer (MODIS) products. In comparison to ground-based SYNOP measurements, we achieved a promising agreement better than 95&thinsp;% and 83&thinsp;% within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for the identification of clouds in a ground scene observed at high latitudes.</p