Scaling Analysis of Ocean Surface Turbulent Heterogeneities from Satellite Remote Sensing : Use of 2D Structure Functions

International audienceSatellite remote sensing observations allow the ocean surface to be sampled synopticallyover large spatio-temporal scales. The images provided from visible and thermal infraredsatellite observations are widely used in physical, biological, and ecological oceanography.The present work proposes a method to understand the multi-scaling properties of satelliteproducts such as the Chlorophyll-a (Chl-a), and the Sea Surface Temperature (SST), rarelystudied. The specific objectives of this study are to show how the small scale heterogeneitiesof satellite images can be characterised using tools borrowed from the fields of turbulence.For that purpose, we show how the structure function, which is classically used in theframe of scaling time series analysis, can be used also in 2D. The main advantage of thismethod is that it can be applied to process images which have missing data. Based on bothsimulated and real images, we demonstrate that coarse-graining (CG) of a gradient modulustransform of the original image does not provide correct scaling exponents. We show,using a fractional Brownian simulation in 2D, that the structure function (SF) can be usedwith randomly sampled couple of points, and verify that 1 million of couple of points providesenough statistics