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System vicarious calibration for ocean color climate change applications: Requirements for in situ data

Abstract : System Vicarious Calibration (SVC) ensures a relative radiometric calibration to satellite ocean color sensors that minimizes uncertainties in the water-leaving radiance L w derived from the top of atmosphere radiance L T. This is achieved through the application of gain-factors, g-factors, to pre-launch absolute radiometric calibration coefficients of the satellite sensor corrected for temporal changes in radiometric sensitivity. The g-factors are determined by the ratio of simulated to measured spectral L T values where the former are computed using: i. highly accurate in situ L w reference measurements; and ii. the same atmospheric models and algorithms applied for the atmospheric correction of satellite data. By analyzing basic relations between relative uncertainties of L w and L T , and g-factors consistently determined for the same satellite mission using different in situ data sources, this work suggests that the creation of ocean color Climate Data Records (CDRs) should ideally rely on: i. one main long-term in situ calibration system (site and radiometry) established and sustained with the objective to maximize accuracy and precision over time of g-factors and thus minimize possible biases among satellite data products from different missions; and additionally ii. unique (i.e., standardized) atmospheric model and algorithms for atmospheric correction to maximize cross-mission consistency of data products at locations different from that supporting SVC. Finally, accounting for results from the study and elements already provided in literature, requirements and recommendations for SVC sites and field radiometric measurements are streamlined.
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https://hal-insu.archives-ouvertes.fr/insu-03190167
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Submitted on : Tuesday, April 6, 2021 - 9:52:48 AM
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Giuseppe Zibordi, Frédéric Mélin, Kenneth Voss, B. Carol Johnson, Bryan Franz, et al.. System vicarious calibration for ocean color climate change applications: Requirements for in situ data. Remote Sensing of Environment, Elsevier, 2015, 159, pp.361-369. ⟨10.1016/j.rse.2014.12.015⟩. ⟨insu-03190167⟩

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