The Coastal Observing System for Northern and Arctic Seas (COSYNA)

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

An ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiate (OC-CCI)

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea viewingWide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel

The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters

The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also waters reaching the frontiers of marine optical remote sensing, namely extreme absorbing, or scattering waters. Considered chlorophyll concentrations reach up to 200 mg m−3, non-algae particle concentrations up to 1,500 g m−3, and the absorption coefficient of colored dissolved organic matter at 440 nm is up to 20 m−1. The algorithm generates different concentrations of water constituents, inherent and apparent optical properties, and a color index. In addition, all products are delivered with an uncertainty estimate. A baseline validation of the products is provided for various water types. We conclude that the algorithm is suitable for the remote sensing estimation of water properties and constituents of most natural waters

Ocean Colour Radiometry and Water Quality

In coastal and marine waters, the variables associated with a defined 'quality status' can change over a wide range of spatial and temporal scales, leading to substantial logistic and economic difficulties to monitor them on a regular basis. Realistically, the approach to coastal water management is to undertake ongoing assessments of the population and condition of the marine flora and fauna, as well as what might be termed 'water quality'. This approach usually employs in situ techniques which rarely sustain the repeat frequency and spatial coverage that is required to support an effective management practice in vulnerable areas, where more intensive monitoring scheme needs to be in place. However, several of the water quality indicators are directly or indirectly related to the colour or reflectance of the water, and therefore, potentially accessible from satellite remote sensing.JRC.H.3-Global environement monitorin

ocOC - from Ocean Colour to Organic Carbon

Enhanced permafrost warming and increased arctic river discharges have heightened concern about the input of terrigenous matter into Arctic coastal waters. The IPY project 'ocOC- from Ocean Colour to Organic Carbon' (2008-2010) uses Ocean Colour (OC) data for synoptic monitoring of the input of terrigenous Organic Carbon (OC) from fluvial and coastal sources into Arctic coastal waters. Every late summer, Russian-German ship expeditions take part in the southern Laptev Sea (Arctic Siberia, Russia). The multi-year expedition data are the base for understanding the optico-chemico properties of the coastal waters. The coastal waters are characterized by low transparencies, resuspension events and high cDOM concentrations. The Laptev Sea Region has become an ESA CoastColour investigation site to support the use of the ground data. Ocean Colour MERIS data from 2008 on to 2010 are processed using the VISAT Beam Case2Regional Processor (C2R). The expedition data show that Siberian Arctic coastal waters are highly specific in terms of high cDOM background concentrations. Therefore, all remote sensing chlorophyll products are overestimated by an order of magnitude due to the high cDOM concentrations. The optical C2R parameters such as absorption, attenuation and the first attenuation depth are of immediate value to show the hydrographic dynamics of the Laptev Sea coastal water

OCoc - from Ocean Colour to Organic Carbon

The terrigenous carbon export into the Arctic shelf systems is a major component of the Arctic Organic Carbon (OC) cycle. Mac Guire et al.(2009)in their review on the Arctic Carbon Cycle recommendate to strengthen observations and design the research sector of 'scaling' that is a key challenge to link the processes observed and understood on fine scales to larger scales, e.g., needed for modeling. Here, remote sensing observations can become important tools. Recent development of satellite ocean color sensors such as MODIS, SeaWiFS, MERIS has been accompanied by an increased effort to establish Ocean Colour (OC) algorithms (e.g., for chlorophyll, suspended matter, coloured dissolved organic matter). The ‘OCoc-from Ocean Colour to Organic Carbon’ project (IPY-project 1176), funded by the German Research Foundation (DFG), is an Ocean Colour study joined with the Arctic Coastal Dynamics ACD network and Arctic Circum-polar Coastal Observatory Network ACCO-Net (IPY-project 90). OCoc uses MERIS data for synoptical monitoring of terrigenous suspended and organic matter in the late-summer ice-free waters of the Laptev See region. MERIS Reduced Resolution (RR)-LIB data are processed towards optical aquatic parameters using Beam-Visat4.2 and the MERIS Case2 Regional processor for coastal application (C2R). Calculated aquatic parameters are optical coefficients and calculated concentrations of chlorophyll, total suspended matter and coloured dissolved organic matter absorption from the water leaving reflectances. The Laptev Sea is characterized by a very shallow topography and considerable Regions of Fresh water Influence ROFIs. The maximum river discharge of the Lena River, the second largest Arctic river in terms of annual fresh water discharge happens during the spring ice-breakup in June. Fluvial systems serve as point sources for high fluxes of dissolved and particulate terrigenous materials. The Laptev Sea coast is a highly dynamic mainly sedimentary ice-rich system that delivers vast amounts of interstorage carbon and old carbon from syncryogenic deposits. Initial comparisons with expedition data (cDOM, transparency, SPM, turbidity, chlorophyll) from the German-Russian TRANSDRIFT expeditions and from German-Russian expeditions at the Laptev Sea Coast (2008 to 2010) are presented. MERIS-C2R optical parameters such as the first attenuation depth, ’Z90’, seem adequately to represent true conditions. Whereas the derived concentration parameters seem to be overestimated. The synoptic information of the optical MERIS-C2R parameters offers an immediate wealth of information. The spatial patterns of the processed MERIS C2R time series show the inter-annual scale of the atmospherically driven circulation patterns. On event scales, we need to investigate if weather patterns potentially contribute to short pulses and circulation patterns