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Assessing the Potential Benefits of the Geostationary Vantage Point for Generating Daily Chlorophyll-a Maps in the Baltic Sea ArchiMer
Bellacicco, Marco; Ciani, Daniele; Doxaran, David; Vellucci, Vincenzo; Antoine, David; Wang, Menghua; D'Ortenzio, Fabrizio; Marullo, Salvatore.
Currently, observations from low-Earth orbit (LEO) ocean color sensors represent one of the most used tools to study surface optical and biogeochemical properties of the ocean. LEO observations are available at daily temporal resolution, and are often combined into weekly, monthly, seasonal, and annual averages in order to obtain sufficient spatial coverage. Indeed, daily satellite maps of the main oceanic variables (e.g., surface phytoplankton chlorophyll-a) generally have many data gaps, mainly due to clouds, which can be filled using either Optimal Interpolation or the Empirical Orthogonal Functions approach. Such interpolations, however, may introduce large uncertainties in the final product. Here, our goal is to quantify the potential benefits of...
Tipo: Text Palavras-chave: Remote sensing; Ocean color products; Geostationary sensor; Baltic Sea.
Ano: 2018 URL: https://archimer.ifremer.fr/doc/00585/69693/67578.pdf
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Quantifying the Impact of Linear Regression Model in Deriving Bio-Optical Relationships: The Implications on Ocean Carbon Estimations ArchiMer
Bellacicco, Marco; Vellucci, Vincenzo; Scardi, Michele; Barbieux, Marie; Marullo, Salvatore; D'Ortenzio, Fabrizio.
Linear regression is widely used in applied sciences and, in particular, in satellite optical oceanography, to relate dependent to independent variables. It is often adopted to establish empirical algorithms based on a finite set of measurements, which are later applied to observations on a larger scale from platforms such as autonomous profiling floats equipped with optical instruments (e.g., Biogeochemical Argo floats; BGC-Argo floats) and satellite ocean colour sensors (e.g., SeaWiFS, VIIRS, OLCI). However, different methods can be applied to a given pair of variables to determine the coefficients of the linear equation fitting the data, which are therefore not unique. In this work, we quantify the impact of the choice of regression method (i.e., either...
Tipo: Text Palavras-chave: Linear regression methods; Bio-optical properties; BGC-Argo; Satellite oceanography.
Ano: 2019 URL: https://archimer.ifremer.fr/doc/00585/69689/67568.pdf
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Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm ArchiMer
Pitarch, Jaime; Bellacicco, Marco; Organelli, Emanuele; Volpe, Gianluca; Colella, Simone; Vellucci, Vincenzo; Marullo, Salvatore.
Particulate optical backscattering (bbp) is a crucial parameter for the study of ocean biology and oceanic carbon estimations. In this work, bbp retrieval, by the quasi-analytical algorithm (QAA), is assessed using a large in situ database of matched bbp and remote-sensing reflectance (Rrs). The QAA is also applied to satellite Rrs (ESA OC-CCI project) as well, after their validation against in situ Rrs. Additionally, the effect of Raman Scattering on QAA retrievals is studied. Results show negligible biases above random noise when QAA-derived bbp is compared to in situ bbp. In addition, Rrs from the CCI archive shows good agreement with in situ data. The QAA’s functional form of spectral backscattering slope, as derived from in situ radiometry, is...
Tipo: Text Palavras-chave: Particulate optical backscattering; Raman scattering; QAA algorithm; ESA OC-CCI.
Ano: 2020 URL: https://archimer.ifremer.fr/doc/00607/71889/70588.pdf
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