Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Two groups of methods exist to estimate water depth from satellite imagery: empirical methods are simple tox000D
implement but require in-situ calibration data, while physics-based methods are more complicated but can bex000D
implemented with the need for in-situ data. The latter are thus necessary for mapping areas where no in-situx000D
data exist and none can feasibly be acquired, such as for the remote Arctic or foreign territory. Physics-basedx000D
methods simultaneously derive information on water depth, seafloor reflectance, and water optical quality, on ax000D
pixel-by-pixel basis, by inverting a forward radiative transfer model parameterized with these quantities.x000D
However, when used with multispectral satellite data suffering from imperfect atmospheric correction,x000D
inversion algorithms must be robust to spectrally correlated and spatially uncorrelated noise. Robust algorithmsx000D
may rely on the reasonable assumption of locally constant water quality, and on image-based indications of biasx000D
from atmospheric correction. In this research, we propose to develop and test robust inversion algorithms thatx000D
improve bathymetry retrievals from multispectral satellite data. We will do this by using low-altitude airbornex000D
hyperspectral data coincident with satellite overpasses to identify and isolate atmospheric correction biases, andx000D
by developing stronger constraints on inversion, for example deriving from assumptions of regionallyx000D
homogeneous water quality ranging from an optically deep area (where the seafloor does not influence thex000D
colour of the area as seen from space) to a nearby optically shallow area targeted for bathymetry mapping. Thex000D
research will improve bathymetry mapping from multispectral satellite images, increasing the value ofx000D
satellite-derived bathymetry in Canada and beyond.