Inferring missing data in satellite chlorophyll maps using turbulent cascading

Abstract: Oceanic turbulent flows develop complicated patterns, with eddies, filaments and shear currents. Although usually referred as chaotic, their inner organization is strongly hierarchical: turbulent flows develop cascades, which transfer properties such as energy or scalar density from larger to smaller scales. In this work, we present a novel algorithm based on the cascade and able to fill data gaps in satellite images (particularly, chlorophyll concentration maps). The first step is to show that cascade processes for chlorophyll-a concentration images take a simple, explicit form when an appropriate wavelet (here Battle–Lemarié of order 3) representation is used. A reconstruction algorithm exploiting the cascade structure is then given with a detailed description. We discuss the validity and quality of this algorithm when applied to SeaWiFS and MODIS-Aqua ocean color images. An application to merging data from multiple satellite missions is presented together with a demonstration of the benefit of this algorithm over two other merging methods. [Copyright &y& Elsevier]