FN Archimer Export Format PT J TI Joint sun-glitter and radar imagery of surface slicks BT AF KUDRYAVTSEV, Vladimir MYASOEDOV, Alexander CHAPRON, Bertrand JOHANNESSEN, Johnny A. COLLARD, Fabrice AS 1:1,2,4;2:1,2;3:3;4:4,5;5:6; FF 1:;2:;3:PDG-ODE-LOS;4:;5:; C1 Russian State Hydrometeorol Univ, St Petersburg, Russia. Nansen Int Environm & Remote Sensing Ctr, St Petersburg, Russia. IFREMER, Plouzane, France. Nansen Environm & Remote Sensing Ctr, Bergen, Norway. Univ Bergen, Inst Geophys, N-5020 Bergen, Norway. CLS, Direct Radar Applicat, Plouzane, France. C2 UNIV RUSSIAN STATE HYDROMETEOROL, RUSSIA NIERSC, RUSSIA IFREMER, FRANCE NERSC, NORWAY UNIV BERGEN, NORWAY CLS, FRANCE SI BREST SE PDG-ODE-LOS IN WOS Ifremer jusqu'en 2018 copubli-france copubli-europe copubli-int-hors-europe IF 5.1 TC 27 UR https://archimer.ifremer.fr/doc/00083/19439/17217.pdf LA English DT Article DE ;Sun-glitter;Mean square slope;Surface slicks;Oil spills;SAR imaging model;SAR and optical synergy AB A method is proposed to retrieve and interpret fine spatial variations of the sea surface roughness in sun glitter imagery. Observed sun glitter brightness anomalies are converted using a transfer function determined from the smoothed shape of sun glitter brightness. The method is applied to MODIS and MERIS sun glitter imagery of natural oil seeps and the catastrophic Deepwater Horizon oil spill in the Gulf of Mexico. The short-scale roughness variations in the presence of mineral oils slicks are consistently extracted and compared to variations associated with the biogenic slicks. In doing so, the wind speed dependency on the roughness anomalies is also considered. A comparison to normalized radar cross section (NRCS) anomalies taken from the corresponding high resolution ASAR images is performed, and similarities as well as differences are investigated. The results document significant benefit from the synergetic use of sun glitter and radar imagery for detection and monitoring of surface slicks. (C) 2012 Elsevier Inc. All rights reserved. PY 2012 PD MAY SO Remote Sensing Of Environment SN 0034-4257 PU Elsevier Science Inc VL 120 UT 000303300000012 BP 123 EP 132 DI 10.1016/j.rse.2011.06.029 ID 19439 ER EF