Satellite-Derived Bathymetry for Canada Anders Knudby 1 , Dulal Roy - - PowerPoint PPT Presentation

Satellite-Derived Bathymetry for Canada

Anders Knudby1, Dulal Roy2, Shahryar Ahmad3, Stephen Bird4, Christopher Ilori3

1 Department of Geography, University of Ottawa 2 Department of Geography, Simon Fraser University 3 Department of Civil Engineering, Indian Institute of Technology Kanpur 4 Fluvial Systems Research

Three main approaches to SDB, based on…

Ocean colour Photogrammetry Waves

Boundary Bay, BC Georgian Bay, ON Kornati National Park, Croatia

Study areas:

( Iqaluit, NU )

Study areas:

Boundary Bay Georgian Bay Kornati NP

Empirical approaches to SDB

Chumbe Island, Tanzania

Range of water quality parameters derived from VIIRS products Forward modeling of above-surface remote sensing reflectance - Rrs(0+)

Physics-based (RTM inversion) approach

Measured seafloor reflectance spectra Depth range fixed from 0.01 m to 10 m Pixel-by-pixel inversion Atmospheric/adjacency correction

• f Landsat 8 image

De-glinting Conversion to Rrs(0+)

Physics-based approach

Depth Water colour (a, b) Seafloor type/reflectance 0.1 0.1, 0.1 Sand 0.1 0.1, 0.2 Sand 0.1 0.1, 0.1 Eelgrass 0.1 0.1, 0.2 Eelgrass 0.2 0.1, 0.1 Sand 0.2 0.1, 0.2 Sand 0.2 0.1, 0.1 Eelgrass 0.2 0.1, 0.2 Eelgrass 0.3 0.1, 0.1 Sand 0.3 0.1, 0.2 Sand 0.3 0.1, 0.1 Eelgrass . . .

Boundary Bay field test results (Landsat 8)

Boundary Bay field test results

But how much (a (and where) can we trust it??? Field validation

Per-pixel uncertainty assessment

Range of water quality parameters derived from VIIRS products Forward modeling of above-surface remote sensing reflectance - Rrs(0+)

Simulations

Measured seafloor reflectance spectra Depth range fixed from 0.01 m to 10 m Derivation of sensor-environment noise as Rrs(0+)-equivalent standard deviation of de- glinted satellite observations over homogeneous deep water Addition of sensor-environment noise to forward-modeled spectra Model inversion

Simulations

Depth Water colour (a, b) Seafloor type/reflectance 0.1 0.1, 0.1 Sand 0.1 0.1, 0.2 Sand 0.1 0.1, 0.1 Eelgrass 0.1 0.1, 0.2 Eelgrass 0.2 0.1, 0.1 Sand 0.2 0.1, 0.2 Sand 0.2 0.1, 0.1 Eelgrass 0.2 0.1, 0.2 Eelgrass 0.3 0.1, 0.1 Sand 0.3 0.1, 0.2 Sand 0.3 0.1, 0.1 Eelgrass . . . Original expected colour Noise- perturbed colour

Simulation results

2.5 and 97.5 percentiles of predicted depths Boundary Bay Better water quality, high sun Better water quality, low sun

Simulation results

Simulation assumes best-case scenario and typical RTM inversion approach:

• Perfect atmospheric correction
• Perfect conversion between diffuse/directional and above/below surface radiation

field

• Perfect (and perfectly parametrized) RTM

Simulation allows us to test things like:

• The influence of water quality
• Variation between sensors (noise and spectral bands)
• Variations in sun angle

Additional potential for uncertainty assessment – optical closure (Brando et al. 2009)

Modeled Observed

Additional potential for uncertainty assessment – substratum detectability (Brando et al. 2009)

Amount of signal resulting from reflection off the seafloor

earthzine.org

• Many more field tests (with proper instrumentation)
• Sensor comparison (WorldView, Sentinel-2, EnMAP)
• Bells and whistles to improve accuracy
• Field-validated per-pixel uncertainty product (TVU)
• Increase level of automation
• Detection of inconsistencies btw. charts and SDB
• Regular monitoring of areas needing attention

To do

Acknowledgements:

aknudby@uottawa.ca