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  • 1
    Publication Date: 2018-10-29
    Description: Given that the commonly used parameter obtained directly from hyperspectral earth observation sensors is the remote sensing reflectance (Rrs), we focused on identification of dominant phytoplankton groups by using Rrs spectra directly. Based on five standard absorption spectra representing five different phytoplankton spectral groups, a simulated database of Rrs (C2X database, compiled within the ESA SEOM C2X Project) that includes 105 different water optical conditions was built with HydroLight. In our previous study we have proposed an identification approach to determine phytoplankton groups with the use of simulated C2X data, and the skill of the identification were also tested by investigating how and to what extend water optical constituents (Chl, NAP, and CDOM) impact the accuracy of this identification (Xi et al. 2017). To furthermore test whether the approach is applicable in various natural waters, we have collected a large set of in situ data from waters with different optical types, including coastal waters such as the German Bight and British coastal waters, and inland waters such as Elbe River and several lakes in Germany. Both in situ Rrs and absorption spectra (ap) are used to identify the dominating phytoplankton group in these waters. Identification results from both approaches are compared, and the identification performance of the Rrs-based approach can therefore be evaluated for natural water applications.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 2
    Publication Date: 2018-11-21
    Description: A critical step for obtaining accurate retrievals of ocean colour remote sensing over waters from hyperspectral imagery is an effective atmospheric correction. Opposed to multispectral imagery, atmospheric scattering and absorbers have to be considered differently at the various spectral bands. Another challenge is the low signal of most water surfaces, which makes the atmospheric correction a crucial task to derive for the hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program), with its expected signal-to-noise ratio, reliable water leaving reflectance measurements. The major goal of this project, ACENMAP, is to develop an efficient atmospheric correction over water with defined uncertainties. With simulated data by the coupled atmosphere-ocean radiative transfer model (RTM) SCIATRAN, atmospheric absorbing and scattering effects on TOA reflectance can be precisely located and accounted for in the correction scheme, as well as other effects as glint and due to the proximity to the coast (e.g. mixed land-water pixels). These simulations will also be used to develop a correction scheme for these effects, as well as for estimating water leaving reflectance from TOA reflectance data. The uncertainty will be derived from RTM simulations, intercomparison and validation with in situ water leaving reflectance and satellite TOA reflectance from multispectral sensors (e.g. MERIS). The developed algorithm will be tested on HICO and SCIAMACHY data (downscaled to EnMAP spectral resolution but keeping the spatial resolution) before EnMAP operation. After verification, the atmospheric correction scheme allowing the retrieval of water leaving reflectance will be implemented into the EnMAP box.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 3
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    Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten
    Publication Date: 2013-08-08
    Description: COSYNA stands for 'Coastal Observing System for Northern and Arctic Seas'. The principal goal of the COSYNA-Project is the construction of a long-term observatory for the German part of the NorthSea. The main task of the COSYNA data management is the integration of different data sources in order to provide a web based visualisation and access to observational as well as forecast data. The usage of standards like OGC standards or community standards like NetCDF, OPeNDAP or ncWMS is essential for the data management in COSYNA. The data are stored in an Oracle database for time series-like data and as NetCDF files for data which are acquired simultaneously at different locations. The time series-like data can be freely accessed using OGC SOS (Sensor Observation Service) whereas NetCDF files are available via OPeNDAP. This concept of ‘Open Data’ within COSYNA is planned for near real time data without quality control.
    Description: Abstract
    Description: Proceedings of the Data Management Workshop, 29-30 October 2009, University of Cologne, Germany, Kölner Geographische Arbeiten, 90, pp. 19-25
    Description: SeriesInformation
    Keywords: Observational Data ; Forecast Data ; Visualization ; Data Management
    Language: English
    Type: Text , Workshop paper
    Format: 1178 Kilobytes
    Format: 7 Pages
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  • 4
    Publication Date: 2019-05-17
    Description: The Coastal Observing System for Northern and Arctic Seas (COSYNA) was established in order to better understand the complex interdisciplinary processes of northern seas and the Arctic coasts in a changing environment. Particular focus is given to the German Bight in the North Sea as a prime example of a heavily used coastal area, and Svalbard as an example of an Arctic coast that is under strong pressure due to global change. The COSYNA automated observing and modelling system is designed to monitor real-time conditions and provide short-term forecasts, data, and data products to help assess the impact of anthropogenically induced change. Observations are carried out by combining satellite and radar remote sensing with various in situ platforms. Novel sensors, instruments, and algorithms are developed to further improve the understanding of the interdisciplinary interactions between physics, biogeochemistry, and the ecology of coastal seas. New modelling and data assimilation techniques are used to integrate observations and models in a quasi-operational system providing descriptions and forecasts of key hydrographic variables. Data and data products are publicly available free of charge and in real time. They are used by multiple interest groups in science, agencies, politics, industry, and the public.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 5
    Publication Date: 2019-07-13
    Description: The Ocean Colour Climate Change Initiative intends to provide a long-term time series of ocean colour data and investigate the detectable climate impact. A reliable and stable atmospheric correction procedure is the basis for ocean colour products of the necessary high quality. In order to guarantee an objective selection from a set of four atmospheric correction processors, the common validation strategy of comparisons between in-situ and satellite derived water leaving reflectance spectra, is extended by a ranking system. In principle, the statistical parameters such as root mean square error, bias, etc. and measures of goodness of fit, are transformed into relative scores, which evaluate the relationship of quality dependent on the algorithms under study. The sensitivity of these scores to the selected database has been assessed by a bootstrapping exercise, which allows identification of the uncertainty in the scoring results. Although the presented methodology is intended to be used in an algorithm selection process, this paper focusses on the scope of the methodology rather than the properties of the individual processors.
    Keywords: Oceanography; Numerical Analysis
    Type: GSFC-E-DAA-TN23655 , Remote Sensing of Environment; 162; 242-256
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  • 6
    Publication Date: 2019-07-13
    Description: Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semianalytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN23643 , Remote Sensing of Enviornment; 162; 271-294
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  • 7
    Publication Date: 2019-07-13
    Description: The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses. The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually. These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms. A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor. Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement.
    Keywords: Earth Resources and Remote Sensing; Oceanography
    Type: GSFC-E-DAA-TN23656 , Remote Sensing of the Enviornment; 162; 257-270
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  • 8
    Publication Date: 2019-07-30
    Description: A central aspect of coastal biogeochemistry is to determine how nutrients, lithogenic- and organic matter are distributed and transformed within coastal and estuarine environments. Analyses of the spatio-temporal changes of total suspended matter (TSM) concentration indicate strong and variable linkages between intertidal fringes and pelagic regions. In particular, knowledge about the organic fraction of TSM provides insight to how biogenic and lithogenic particulate matter are distributed in suspension. In our study we take advantage of a set of over 3000 in situ Loss on Ignition (LoI) data from the Southern North Sea that represent fractions of particulate organic matter (POM) relative to TSM (LoI $\equiv$ POM:TSM). We introduce a parameterization (POM-TSM model) that distinguishes between two POM fractions incorporated in TSM. One fraction is described in association with mineral particles. The other represents a seasonally varying fresh pool of POM. The performance of the POM-TSM model is tested against data derived from MERIS/ENVISAT-TSM products of the German Bight. Our analysis of remote sensing data exhibits specific qualitative features of TSM that can be attributed to distinct coastal zones. Most interestingly, a transition zone between the Wadden Sea and seasonally stratified regions of the Southern North Sea is identified where mineral associated POM appears in concentrations comparable to those of freshly produced POM. We will discuss how this transition is indicative for a zone of effective particle interaction and sedimentation.The dimension of this transition zone varies between seasons and with location. Our proposed POM-TSM model is generic and can be calibrated against in situ data of other coastal regions.
    Type: Article , PeerReviewed
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  • 9
    Publication Date: 2019-07-30
    Description: Chlorophyll (chl a) concentration in coastal seas exhibits variability on various spatial and temporal scales. Resuspension of particulate matter can somewhat limit algal growth, but can also enhance productivity because of the intrusion of nutrient-rich pore water from sediments or bottom water layers into the whole water column. This study investigates whether characteristic changes in net phytoplankton growth can be directly linked to resuspension events within the German Bight. Satellite-derived chl a were used to derive spatial patterns of net rates of chl a increase/decrease (NR) in 2003 and 2004. Spatial correlations between NR and mean water column irradiance were analysed. High correlations in space and time were found in most areas of the German Bight (R2 〉 0.4), suggesting a tight coupling between light availability and algal growth during spring. These correlations were reduced within a distinct zone in the transition between shallow coastal areas and deeper offshore waters. In summer and autumn, a mismatch was found between phytoplankton blooms (chl a 〉 6 mg m−3) and spring-tidal induced resuspension events as indicated by bottom velocity, suggesting that there is no phytoplankton resuspension during spring tides. It is instead proposed here that frequent and recurrent spring-tidal resuspension events enhance algal growth by supplying remineralized nutrients. This hypothesis is corroborated by a lag correlation analysis between resuspension events and in-situ measured nutrient concentrations. This study outlines seasonally different patterns in phytoplankton productivity in response to variations in resuspension, which can serve as a reference for modelling coastal ecosystem dynamics.
    Type: Article , PeerReviewed
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  • 10
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