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Open AccessArticle

Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm

1
Department of Coastal Systems, NIOZ Royal Netherlands Institute for Sea Research and Utrecht University, 1790 Texel, The Netherlands
2
Istituto di Scienze del Mare (ISMAR)-CNR, Via Fosso del Cavaliere, 100 Rome, Italy
3
Energy and Sustainable Economic Development (ENEA), Italian National Agency for New Technologies, 00044 Frascati, Italy
4
Laboratoire d’Océanographie de Villefranche (LOV), Sorbonne University, CNRS, 06230 Villefranche-sur-Mer, France
5
Institut de la Mer de Villefranche (IMEV), Sorbonne University, CNRS, F-06230 Villefranche-sur-Mer, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 77; https://doi.org/10.3390/rs12010077
Received: 29 November 2019 / Revised: 20 December 2019 / Accepted: 20 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Satellite Derived Global Ocean Product Validation/Evaluation)
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 validated. Finally, we show the importance of correcting for Raman Scattering over clear waters prior to semi-analytical retrieval. Overall, this work demonstrates the high efficiency of QAA in the bbp detection in case of both in situ and ocean color data, but it also highlights the necessity to increase the number of observations that are severely under-sampled in respect to others environmental parameters. View Full-Text
Keywords: particulate optical backscattering; Raman scattering; QAA algorithm; ESA OC-CCI particulate optical backscattering; Raman scattering; QAA algorithm; ESA OC-CCI
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MDPI and ACS Style

Pitarch, J.; Bellacicco, M.; Organelli, E.; Volpe, G.; Colella, S.; Vellucci, V.; Marullo, S. Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm. Remote Sens. 2020, 12, 77.

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