Satellite Ocean-Color Remote Sensing in the Presence of Clouds
Robert
J.
Frouin, Scripps Institution of Oceanography, rfrouin@ucsd.edu
(Presenting)
Pierre-Yves
Deschamps, University of Lille, pyd@loa.univ-lille1.fr
Lydwine
Gross, CapGemini, lydwine.grosscolzy@capgemini.com
Hiroshi
Murakami, Japanese Aerospace Exploration Agency, murakami.hiroshi.eo@jaxa.jp
Lucile
Duforêt, University of Littoral Côte d'Opale, lucile.duforet@univ-littoral.fr
Ocean-color remote sensing from space is currently limited to cloud-free areas. Consequently, the daily ocean coverage is 15-20%, and weekly products show no information in many areas. This limits considerably the utility of satellite ocean-color observations for operational oceanography. Global coverage is required every three to five days in the open ocean and at least every day in the coastal zone. In view of the requirements for spatial coverage, and of the effects of clouds on observations of ocean color, algorithms are proposed to estimate marine reflectance or chlorophyll concentration directly in the presence of a thin or broken cloud layer. The algorithms exploit that atmospheric and surface contributions to the top-of-atmosphere reflectance, including cloud reflectance and interactions between droplets, molecules, and aerosols can be modeled accurately, whatever the cloud geometry, as a simple polynomial or using principal components. By combining spectral bands linearly, or by selecting the principal components sensitive to the ocean signal, the perturbing effects are substantially reduced. Applying the algorithms to actual satellite ocean-color imagery, a substantial gain in ocean coverage is obtained. The oceanic features retrieved below the clouds exhibit continuity with the adjacent features in clear areas. Retrieved marine reflectance and chlorophyll concentration agree with estimates from standard algorithms. Daily ocean coverage is expected to increase to up to 50% with the proposed algorithms. This could lead to important new information about the temporal variability of biological processes.
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