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  • Other Sources  (3)
  • 1
    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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  • 2
    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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  • 3
    In:  In: Remote Sensing of the Asian Seas. , ed. by Barale, V. and Gade, M. Springer, Cham, pp. 123-138. ISBN 978-3-319-94065-6
    Publication Date: 2019-09-23
    Description: The Laptev and Eastern Siberian shelves are the world’s broadest shallow shelf systems. Large Siberian rivers and coastal erosion of up to meters per summer deliver large volumes of terrestrial matter into the Arctic shelf seas. In this chapter we investigate the applicability of Ocean Colour Remote Sensing during the ice-free summer season in the Siberian Laptev Sea region. We show that the early summer river peak discharge may be traced using remote sensing in years characterized by early sea-ice retreat. In the summer time after the peak discharge, the spreading of the main Lena River plume east and north-east of the Lena River Delta into the shelf system becomes hardly traceable using optical remote sensing methods. Measurements of suspended particulate matter (SPM) and coloured dissolved organic matter (cDOM) are of the same magnitude in the coastal waters of Buor Khaya Bay as in the Lena River. Match-up analyses of in situ chlorophyll-a (Chl-a) show that standard Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived Chl-a is not a valid remote sensing product for the coastal waters and the inner shelf region of the Laptev Sea. All MERIS and MODIS-derived Chl-a products are overestimated by at least a factor of ten, probably due to absorption by the extraordinarily high amount of non-algal particles and cDOM in these coastal and inner-shelf waters. Instead, Ocean Colour remote sensing provides information on wide-spread resuspension over shallows and lateral advection visible in satellite-derived turbidity. Satellite Sea Surface Temperature (SST) data clearly show hydrodynamics and delineate the outflow of the Lena River for hundreds of kilometres out into the shelf seas.
    Type: Book chapter , NonPeerReviewed
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