Poster Session 1, Monday, October 3, 15:40–18:00
Method to maximize data recovery using multi-satellite composites and detect island mass effect
Satellite observations in visible and near-infrared are, at the very least, partially obstructed by clouds or suffer from glint, thus preventing full data recovery. Full image recovery is usually achieved by averaging over space (reducing spatial resolution) or time (16 days or monthly averages). These two methods preclude the detection of short-lived (< 8 days) and small spatial-scale processes (<10 km; island mass effect, river plumes, sub-mesoscale filaments around eddies). Several ocean color satellites were launched in the past 10 years, augmenting the observations made by the Moderate Resolution Imaging Spectroradiometer (MODIS) since 2000. We present here a method for maximizing data recovery at the nominal spatial resolution of 1 km while minimizing time averaging. We re-produced the processing pipeline of NASA Ocean Color to obtain 1-km resolution level-3 custom-made composites of Inherent Optical Properties (IOP) from level-1A uncalibrated data of three different sensor types (MODIS, VIIRS, and OLCI), onboard up to 6 polar-orbiting satellites (Aqua, Terra, SNPP, JPSS1, Sentinel-3a & b). Satellite IOPs were validated against in-situ measurements of IOPs from a continuous in-line system and pigment estimates from high precision liquid chromatography before being merged into 3-day averages to recover full, or nearly full images. These composite products were used to characterize the bio-optical seascape around islands and detect island mass effects. This method shows the potential for both high density and high-quality products attainable with the current set of ocean color satellites and their relevance for the study of sub-mesoscale processes.
Emmanuel Boss, University of Maine, [email protected], 0000-0002-8334-9595
Andrew Thomas, University of Maine, [email protected]
Lee Karp-Boss, University of Maine, [email protected], 0000-0003-2851-1921
Fabien Lombard, University of Maine, [email protected], 0000-0002-8626-8782