Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters

Type Article
Date 2017-01
Language English
Author(s) Novoa Stefani1, Doxaran David1, Ody Anouck1, Vanhellemont Quinten2, Lafon Virginie3, Lubac BertrandORCID4, Gernez Pierre5
Affiliation(s) 1 : UPMC, CNRS, UMR7093, Lab Oceanog Villefranche, 181 Chemin Lazaret, F-06230 Villefranche Sur Mer, France.
2 : Royal Belgian Inst Nat Sci, B-1000 Brussels, Belgium.
3 : Univ Bordeaux, UMR 5805, GEO Transfert, EPOC, Allee Geoffroy St Hilaire, F-33615 Pessac, France.
4 : Univ Bordeaux, OASU, CNRS, UMR 5805,EPOC, Site Talence,Batiment B18, F-33615 Bordeaux, France.
5 : Univ Nantes, EA 2160, MMS, 2 Rue Houssiniere BP 92208, F-44322 Nantes 3, France.
Source Remote Sensing (2072-4292) (Mdpi Ag), 2017-01 , Vol. 9 , N. 1 , P. 61 (31p.)
DOI 10.3390/rs9010061
WOS© Times Cited 67
Note This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing
Keyword(s) remote sensing, suspended particulate matter, coastal waters, river plumes, multi-conditional algorithm
Abstract The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal ((w)) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. (w) relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in (w) retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries' monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions.
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Novoa Stefani, Doxaran David, Ody Anouck, Vanhellemont Quinten, Lafon Virginie, Lubac Bertrand, Gernez Pierre (2017). Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters. Remote Sensing, 9(1), 61 (31p.). Publisher's official version : https://doi.org/10.3390/rs9010061 , Open Access version : https://archimer.ifremer.fr/doc/00590/70197/