A semi-analytical ocean color inherent optical property model: approach and application.
Tim Smyth, Gerald Moore, Takafumi Hirata and Jim Aiken
Plymouth Marine Laboratory, UK
Overview
• Model description
• Implementation
• Validation
• Sensitivity study - summary
• Application to satellite data
• Further work
1) Model description
• Morel (1980): “ … the inverse system can be theoretically solved using simultaneous equations, if a spectral law is assumed for backscattering.”
– Not implemented (or implementable!) for either in situ or satellite data
• Sugihara & Kishino (1988) and Roesler & Perry (1995): implemented schemes for in-water reflectance data
– Not scaled up to satellite data (problems with Q)
• We have developed a scheme using simultaneous equations: solved using empirically derived spectral slopes in combination with radiative transfer modelling.
Interface terms (Fresnel and f/Q): angular and IOP dependency
Total absorption:
aph(λ), ad(λ), ay(λ)
• Two unknowns of bbp(λ) and a(λ): therefore require two equations … achieve this using two neighbouring wavelengths (i, j) and empirically derived spectral slopes.
Simultaneous equations
1) Model description
Spectral slope in total absorption
Spectral slope in total backscatter
• solve equations simultaneously for a(j) and bbp(j) – nasty maths!
• then can solve for a(i) and bbp(i) using spectral slopes.
• need to work out which wavelength pairing to use for spectral slopes – based on empirical data.
1) Model description
• Spectral slopes from COLORS dataset (predominantly coastal stations)
• εa(490,510) converged to narrow range of values with low σ
• εa(490,510) = 1.268; εbb(490,510) = 1.0202
• Is this because only 20 nm difference between bands?
• observationally: chlorophyll-a has greatest effect between 400 – 470 nm; minor effect between 490 and 510 nm.
N=216
1) Model description
• Once have a(490,510) and bbp(490,510), then use assumed shape of backscatter to extrapolate to other wavelengths:
• can then work out the entire spectrum of a(λ)
• bio-geochemical parameters of ady(λ) and aph(λ) can be determined using spectral slope method
• εdy(412,443) and εph(412,443) selected as they are distinct with low variance. Used in combination with standard CDOM exponential function to extrapolate to other wavelengths.
2) Implementation
3) Validation
• Validation using NOMAD in situ dataset:– Points selected on basis that each entry
contained ρw(SeaWiFS); a(λ); aph(λ) and ady(λ);
– 439 data points met this criterion;
– 88 data points for bbp(λ).
– Comparison with Lee et al. (2002) model
3) Validation: Total absorption (PML model), a(λ)
R2: 0.835RMS: 0.192
R2: 0.851RMS: 0.161
R2: 0.840RMS: 0.202
R2: 0.819RMS: 0.118
R2: 0.637RMS: 0.148
R2:0.061 RMS: 0.362
N=418
Signal to noise ratio?
Raman scattering?
Good retrievals over 2 orders of magnitude
3) Validation: Total absorption (Lee model), a(λ)
R2: 0.549RMS: 0.362
N=439
R2: 0.464RMS: 0.376
R2: 0.206RMS: 0.415
R2: 0.006RMS: 0.435
R2: 0.509RMS: 0.475
R2: 0.556RMS: 0.308
Limitation at higher absorption
3) Validation: Total backscatter, bb(λ)
N=88
R2: 0.400RMS: 0.148
R2: 0.395RMS: 0.148
R2: 0.387RMS: 0.192
R2: 0.383RMS: 0.217
R2: 0.375RMS: 0.270
R2: 0.354RMS: 0.390
Increasing bias with increasing λ: problem with assumed spectral shape?
3) Validation: CDOM absorption, ady(λ)
R2: 0.568RMS: 0.507
R2: 0.532RMS: 0.500
R2: 0.477RMS: 0.516
R2: 0.453RMS: 0.519
R2: 0.406RMS: 0.538
R2: 0.313RMS: 0.595
Noisy at low ady(λ): possible measurement error?
3) Validation: phytoplankton absorption, aph(λ)
R2: 0.666RMS: 0.298
R2: 0.759RMS: 0.230
R2: 0.744RMS: 0.263
R2: 0.677RMS: 0.358
R2: 0.394RMS: 0.857
R2: 0.099RMS: 0.178
Problems with retrievals at 555 and 670
4) Sensitivity study - summary
• a(λ) and bb(λ):
– Model most sensitive to εa(490,510)
• NOMAD 1.317 cf. COLORS 1.268
– Relatively insensitive to εbb(490,510)
• NOMAD 1.040 cf. COLORS 1.0202
• ady(λ) and aph(λ)
– Most sensitive to εph(412,443)
• NOMAD 1.065 cf. COLORS 0.954
– Relatively insensitive to εdy(412,443) and S
• NOMAD 1.638 cf. COLORS 1.579
5) Application to satellite data
• Implemented on HRPT and GAC SeaWiFS imagery using an Intel Xeon 1.8 GHz processor:– 15 mins proc HRPT– 1.5 mins process on GAC entire orbit
Phytoplankton – fine eddy structure
Sediment
Coccolithophores
CDOM
bloom?
Clear blue ocean
18 May 1998 13.14 GMT “True color” composite - qualitative
a) a(443): high values (0.2 – 0.5 m-1) in coastal seas; ca. 0.3 m-1 in bloom.
b) ady(443): coastal seas dominated by CDOM;
c) aph(443): phytoplankton bloom off W. Ireland;
d) bbp(555): Emiliana huxleyi bloom in Western Approaches (in situ confirmed this)
IOP model allows us to quantify these features.
a(443)
bbp(555)aph(443)
ady(443)
10 Oct 2002 10.26 GMT
a(443) aph(443) ady(443)
• BENCAL experiment (October 2002)
• upwelling combination of ady and aph; offshore bloom dominated by aph
South Africa
Namibia
5) Further work
• 490:510 pairing used for SeaWiFS / MERIS– Develop 488:532 spectral slopes for use with MODIS
• 412:443 pair for ady and aph subject to ρw(412) problems (atmospheric correction); could use 443:490 pairing instead
• IOP models can form building block for many applications:– Determination of phytoplankton functional types;– Data assimilation into process oriented models:
address rate equations, cf. chlorophyll which is a model derived variable;
– Primary production modelling without recourse to chlorophyll