Reference number : GC-2017-Q2-00540, amendment number : , agreement type : G, recipient legal name : Knudby, Anders (University of Ottawa)|Knudby, Anders (Université d’Ottawa), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Two groups of methods exist to estimate water depth from satellite imagery: empirical methods are simple to_x000D_
implement but require in-situ calibration data, while physics-based methods are more complicated but can be_x000D_
implemented with the need for in-situ data. The latter are thus necessary for mapping areas where no in-situ_x000D_
data exist and none can feasibly be acquired, such as for the remote Arctic or foreign territory. Physics-based_x000D_
methods simultaneously derive information on water depth, seafloor reflectance, and water optical quality, on a_x000D_
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 algorithms_x000D_
may rely on the reasonable assumption of locally constant water quality, and on image-based indications of bias_x000D_
from atmospheric correction. In this research, we propose to develop and test robust inversion algorithms that_x000D_
improve bathymetry retrievals from multispectral satellite data. We will do this by using low-altitude airborne_x000D_
hyperspectral data coincident with satellite overpasses to identify and isolate atmospheric correction biases, and_x000D_
by developing stronger constraints on inversion, for example deriving from assumptions of regionally_x000D_
homogeneous water quality ranging from an optically deep area (where the seafloor does not influence the_x000D_
colour of the area as seen from space) to a nearby optically shallow area targeted for bathymetry mapping. The_x000D_
research will improve bathymetry mapping from multispectral satellite images, increasing the value of_x000D_
satellite-derived bathymetry in Canada and beyond., program purpose french : Two groups of methods exist to estimate water depth from satellite imagery: empirical methods are simple to_x000D_
implement but require in-situ calibration data, while physics-based methods are more complicated but can be_x000D_
implemented with the need for in-situ data. The latter are thus necessary for mapping areas where no in-situ_x000D_
data exist and none can feasibly be acquired, such as for the remote Arctic or foreign territory. Physics-based_x000D_
methods simultaneously derive information on water depth, seafloor reflectance, and water optical quality, on a_x000D_
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 algorithms_x000D_
may rely on the reasonable assumption of locally constant water quality, and on image-based indications of bias_x000D_
from atmospheric correction. In this research, we propose to develop and test robust inversion algorithms that_x000D_
improve bathymetry retrievals from multispectral satellite data. We will do this by using low-altitude airborne_x000D_
hyperspectral data coincident with satellite overpasses to identify and isolate atmospheric correction biases, and_x000D_
by developing stronger constraints on inversion, for example deriving from assumptions of regionally_x000D_
homogeneous water quality ranging from an optically deep area (where the seafloor does not influence the_x000D_
colour of the area as seen from space) to a nearby optically shallow area targeted for bathymetry mapping. The_x000D_
research will improve bathymetry mapping from multispectral satellite images, increasing the value of_x000D_
satellite-derived bathymetry in Canada and beyond., agreement title english : Image-derived constraints for inversion of physics-based methods in satellite-derived bathymetry, agreement title french : Image-derived constraints for inversion of physics-based methods in satellite-derived bathymetry, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada