This document summarizes a model of surface reflectance and true color for the Great Barrier Reef that was produced using a coupled hydrodynamic, optical, sediment, and biogeochemical model. The model includes 20 optically active components and calculates surface reflectance based on the optical depth weighted reflectance. Comparison to MODIS observations shows small biases and errors relative to spatial variability. The model produces realistic simulations of surface reflectance spectra and true color images that allow visualizing how optical water properties interact with the seafloor, without needing false coloring.

1. Surface reflectance and true colour
produced by a coupled
hydrodynamic, optical, sediment,
biogeochemical model of the Great
Barrier Reef, Australia.
CSIRO OCEANS AND ATMOSPHERE FLAGSHIP
Mark Baird1, Mathieu Mongin1, Nugzar Margvelashvili1, Karen Wild-Allen1, Jenny Skerratt1, Emlyn Jones1
Barbara Robson2, Thomas Schroeder1, Nagur Cherukuru1, Andy Steven1.
1CSIRO Oceans and Atmosphere Flagship, 2CSIRO Land and Water Flagship
Thanks to GISERA, Science Industry Endowment Fund, Great Barrier Reef Foundation and
the eReefs project team.

2. • Bottom sediments / reefs
• Absorption by organic matter
• Phytoplankton blooms (+type)
• River plumes
• Circulation structure
Image shows:

3. • But what we measure from space is
not the same quantity we represent
in the water quality models (e.g.
surface reflectance based MODIS OC3
chl a algorithm is not equal to
phytoplankton biomass).
• This model – observation mis-match
is the most significant obstacle to
assessment and data assimilation in
water quality models.
• The goal of this talk is to present a coupled hydrodynamic / sediment /
optical / biogeochemical model with model output that is the same
quantity as what is observed – surface reflectance.

4. S
S
S
S
S
s
200 m regional models
Hydrodynamic models
• Hydrodynamic models of 4 km
and 1 km forced by global model
and 22 rivers.
• Hindcast from Sep 2010 –
present day.
• Near real time.
• 4 km version resolves
interaction of large rivers, reef
matrix, shelf and open ocean.
• 1 km resolves individual reefs,
river plumes etc.
• 200 m resolution models forced
by GBR4 and GBR1 resolves reef
crest / reef lagoon / river
entrances.
shshshs

5. Presentation title | Presenter name5 |
20 optically – active
components
• clear water
• 4 types of phytoplankton
with two pigment types
each
• CDOM
• macroalgae
• 2 types of seagrass
• coral skeletons
• zooxanthellae
• sediment x 2
• detritus x 3

6. Seagrass in
Moreton Bay
Carbon chemistry
Oxygen
drawdown
in the Fly
R.
Nitrogen
fixation in
GBR lagoon
Increased
vertical
attenuation
in plume
waters
Shelf break
subsurface
Chl max.
Benthic
microalgae
off Mackay

7. Vertical attenuation:
bT, λ
Where w is weighted by optical depth
Reflectance is the ratio of backscatter to
attenuation: In other words:
Surface reflectance is calculated as the optical-depth
weighted reflectance plus a bottom reflectance
weighted by its optical depth.
Irradiance
Depth
Reflectance is function of
through the sea-air interface
Optical model Baird et al submitted
IOPs = adsorption (aT, λ) and
backscattering (bT, λ) of
plankton, CDOM, particles.
Atmospheric forcing
uses spectrally-resolved
energy distribution of
sun light.

8. • Mean simulated
surface
reflectance for
2011 at the 8
MODIS ocean
colour bands.
• More reflectance at
shorter wavelengths.

9. Error in the mean
simulated surface
reflectance for
2011 at the 8
MODIS ocean
colour bands.
Bias and spatially-
averaged error small
relative to spatial
variability or model –
observation mis-match
– thus errors in optics =
errors in
biogeochemical model
Simulated reflectance – observed reflectance (500+ swaths)

10. Atmospherically-corrected
Observation of
top of
atmosphere
(TOA) reflectance
corrected for
atmospheric
scattering and
absorption to
obtain, cloud
permitting,
surface
reflectance as
viewed at a solid
angle, Rrs [sr-1 ]
Optical-depth
weighted reflectance
as a result of 20
optically- active
constituents
initialised 100 days
earlier, and
transported,
biogeochemically-
transformed,
flocculated and
resuspended, to
determine surface
reflectance at any
time, viewed at a
solid angle, Rrs [sr-1 ]

11. Atmospherically-corrected Simulated
Observation of
top of
atmosphere
(TOA) reflectance
corrected for
atmospheric
scattering and
absorption to
obtain, cloud
permitting,
surface
reflectance as
viewed at a solid
angle, Rrs [sr-1 ]
Optical-depth
weighted reflectance
as a result of 20
optically- active
constituents
initialised 100 days
earlier, and
transported,
biogeochemically-
transformed,
flocculated and
resuspended, to
determine surface
reflectance at any
time, viewed at a
solid angle, Rrs [sr-1 ]

14. Spectra of atmospherically-corrected surface reflectance
- dashed is observations; solid is model.
High CDOM
Reef waters
River plume

15. Moore et. al. (2009) water types
Types 1, 2, 4 and 7 ?
Identification of water types – GBR has a great variety
of water types.

16. Identification of water types with 31 model wavelengths
Moore et. al. (2009) water types
Types 1, 2, 4 and 7 ?

17. CIE 1931 color space
Dierssen et al. (2006) LO 51:2646
• Since the optical model is
spectrally-resolved, use 31
wavelengths to tailor RGB
reflectance to the mean human
photoreceptor response.
• Slightly more natural colours,
especially the green.
MODIS
ocean
color
bands
in grey

18. Tropical cyclone Yasi 2011
MODIS true color, 2 Feb 2011

19. Tropical cyclone Yasi 2011
MODIS true color, 2 Feb 2011

20. Conclusions
• Mismatch between observed and modelled optical
quantities is the greatest obstacle to assessing water
quality models.
• We have developed an optical model to compare
simulated and surface reflectance.
• Assessment at 8 MODIS bands is promising, with
errors dominated by biogeochemical model state –
this suitable for model assessment and data
assimilation.
• Simulated true colour allows us to “see” the model
output, without the need for false colouring and / or
expert interpretation – visualising the quantity that
it of most concern to GBR managers, the spectral
quality of the light, and its interaction with the
bottom.

21. Thank you to:
• Hydrodynamic modelling: Mike Herzfeld, Philip
Gillibrand, John Andrewartha, Farhan Rizwi,
Richard Brinkman
•Biogeochemical modelling: Jenny Skerratt,
Mathieu Mongin, Mark Baird, Karen Wild-Allen,
Emlyn Jones, Nugzar Margvelashvili, Barbara
Robson, Malin Gustafsson, Matthew Adams +
more
• Remote sensing: Andy Steven, Thomas
Schroeder, Nagur Cherukuru, Ed King, Ken Suber
•In situ observations: IMOS, AIMS (Miles Furnas,
David McKinnon) , CSIRO (Bronte Tilbrook), CSIRO
Carbon Cluster (Peter Ralph).
• Forcings: Catchment flows (DERM),
Meteorology (BoM).

22. Quantifying errors with observations and in situ observations
Errors in Reflectance dominated
by
BGC errors not the optical model
parameterization
Therefore DA could correct error
that need corrections
“only” 3 times bigger than what
the remote sensing error