A Stochastic Technique for Satellite Ocean-color Remote Sensing
Robert
J.
Frouin, Scripps Institution of Oceanography, rfrouin@ucsd.edu
(Presenting)
Bruno
Pelletier, University of Montpellier, pelletier@math.univ-montp2.fr
Ocean-color remote sensing from space aims at retrieving from a noisy top-of-atmosphere radiance the values taken by relevant variables such as the chlorophyll-a concentration or the marine reflectance. From a mathematical perspective, it is an ill-posed inverse problem with a highly nonlinear operator. Few techniques are available in the case of a nonlinear inverse problem; even its theoretical study is far from easy, yet some techniques may be used in a practical setting when the noise distribution is known. However in ocean-color remote sensing, the noise encompasses several types of error owing to the forward operator approximation (i.e., accuracy of radiative transfer model), as well as to calibration uncertainties and pure measurement noise. Hence the noise distribution is unknown. In this work, a stochastic technique is proposed to first infer a noise distribution, which is next used to retrieve the marine reflectance in a least-square prediction setting by a regression model.
NASA Carbon Cycle & Ecosystems Active Awards Represented by this Poster: