Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor
The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of t...
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doaj-e069fe74aeb7418d8b907bac738597ea2020-11-25T01:20:23ZengMDPI AGRemote Sensing2072-42922019-03-0111666810.3390/rs11060668rs11060668Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color SensorMohamed Abdelillah Mograne0Cédric Jamet1Hubert Loisel2Vincent Vantrepotte3Xavier Mériaux4Arnaud Cauvin5Université du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), F 62930 Wimereux, FranceUniversité du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), F 62930 Wimereux, FranceUniversité du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), F 62930 Wimereux, FranceUniversité du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), F 62930 Wimereux, FranceUniversité du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), F 62930 Wimereux, FranceUniversité du Littoral Côte d’Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG), F 62930 Wimereux, FranceThe Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of the atmosphere from the total measured signal by the remote sensor at the top of the atmosphere. Five ACs: the baseline AC, the Case 2 regional coast color neural network AC, its alternative version, the Polymer AC, and the standard NASA AC, are inter-compared over two bio-optical contrasted French coastal waters. The retrieved water-leaving reflectances are compared with in situ ocean color radiometric measurements collected using an ASD FielSpec4 spectrometer. Statistical and spectral analysis were performed to assess the best-performing AC through individual (relative error (RE) at 412 nm ranging between 23.43 and 57.31%; root mean squared error (RMSE) at 412 nm ranging between 0.0077 and 0.0188) and common (RE(412 nm) = 24.15–50.07%; RMSE(412 nm) = 0.0081–0.0132) match-ups. The results suggest that the most efficient schemes are the alternative version of the Case 2 regional coast color neural network AC with RE(412 nm) = 33.52% and RMSE(412 nm) = 0.0101 for the individual and Polymer with RE(412 nm) = 24.15% and RMSE(412 nm) = 0.0081 for the common ACs match-ups. Sensitivity studies were performed to assess the limitations of the AC, and the errors of retrievals showed no trends when compared to the turbidity and CDOM.http://www.mdpi.com/2072-4292/11/6/668validationatmospheric correctionsensitivity studyocean colorOLCISentinel-3match-ups exerciseASDwater-leaving reflectancecoastal waters |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohamed Abdelillah Mograne Cédric Jamet Hubert Loisel Vincent Vantrepotte Xavier Mériaux Arnaud Cauvin |
spellingShingle |
Mohamed Abdelillah Mograne Cédric Jamet Hubert Loisel Vincent Vantrepotte Xavier Mériaux Arnaud Cauvin Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor Remote Sensing validation atmospheric correction sensitivity study ocean color OLCI Sentinel-3 match-ups exercise ASD water-leaving reflectance coastal waters |
author_facet |
Mohamed Abdelillah Mograne Cédric Jamet Hubert Loisel Vincent Vantrepotte Xavier Mériaux Arnaud Cauvin |
author_sort |
Mohamed Abdelillah Mograne |
title |
Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor |
title_short |
Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor |
title_full |
Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor |
title_fullStr |
Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor |
title_full_unstemmed |
Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor |
title_sort |
evaluation of five atmospheric correction algorithms over french optically-complex waters for the sentinel-3a olci ocean color sensor |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-03-01 |
description |
The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of the atmosphere from the total measured signal by the remote sensor at the top of the atmosphere. Five ACs: the baseline AC, the Case 2 regional coast color neural network AC, its alternative version, the Polymer AC, and the standard NASA AC, are inter-compared over two bio-optical contrasted French coastal waters. The retrieved water-leaving reflectances are compared with in situ ocean color radiometric measurements collected using an ASD FielSpec4 spectrometer. Statistical and spectral analysis were performed to assess the best-performing AC through individual (relative error (RE) at 412 nm ranging between 23.43 and 57.31%; root mean squared error (RMSE) at 412 nm ranging between 0.0077 and 0.0188) and common (RE(412 nm) = 24.15–50.07%; RMSE(412 nm) = 0.0081–0.0132) match-ups. The results suggest that the most efficient schemes are the alternative version of the Case 2 regional coast color neural network AC with RE(412 nm) = 33.52% and RMSE(412 nm) = 0.0101 for the individual and Polymer with RE(412 nm) = 24.15% and RMSE(412 nm) = 0.0081 for the common ACs match-ups. Sensitivity studies were performed to assess the limitations of the AC, and the errors of retrievals showed no trends when compared to the turbidity and CDOM. |
topic |
validation atmospheric correction sensitivity study ocean color OLCI Sentinel-3 match-ups exercise ASD water-leaving reflectance coastal waters |
url |
http://www.mdpi.com/2072-4292/11/6/668 |
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