h. loisel, c. jamet, and d. dessailly
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Recent advances for the inversion of the particulate backscattering coefficient at different wavelengths. H. Loisel, C. Jamet, and D. Dessailly. Philosophy of the LS’s model. - PowerPoint PPT PresentationTRANSCRIPT
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Recent advances for the inversion of the particulate backscattering
coefficient at different wavelengths
H. Loisel, C. Jamet, and D. DessaillyH. Loisel, C. Jamet, and D. Dessailly
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Philosophy of the LS’s model
“The major motivation for the development of the LS’s algorithm was the assessment of the total IOP from basic radiometric measurements by the means of a simple and fast approach that does not require any assumption about the spectral shapes of IOP”.
Loisel and Stramski, 2000
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The LS’s Model for in situ and RS applications • AdvantagesAdvantages
– Does not require assumption about the spectral shape of IOP
– Explicitly accounts for sun angle variation, and the impact of the respective proportion between molecular and particulate scattering in the AOP vs. IOP relationships.
= bw/b
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The LS’s Model for in situ and RS applications
• Limitation for RS Limitation for RS applicationsapplications
– Use R(0-) instead of Rrs
– Kd is not measured from remote sensing but estimated from Rrs
• Original equationsOriginal equations
)]0([101b RKb d
w
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The LP’ (IOCCG, 2006): improvements of the The LP’ (IOCCG, 2006): improvements of the first version of the model for remote sensing first version of the model for remote sensing applicationsapplications::
– The model directly accounts for Rrs instead of R(0-) – We developed a new way (iterative) to account for the effect of h on the derivation of both a and bb
– We performed some slight modifications within the a parameterisation to accounts for some more realistic -b/a combinations at any given wavelengths used by actual ocean color sensors
– We used some new parameterisations between <Kd()>1 and remote sensing ratios for each wavelength.
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Performance of the LP’model at 490 nm using the IOCCG synthetic data set.
With true Kd With estimated Kd
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slope = 0.902 intercept = -0.173 r2 = 0.924
slope = 0.935intercept = -0.114r2 = 0.917
slope = 0.973 intercept = -0.028r2 = 0.934
rmse = 0.140
rmse =0.138
rmse = 0.123
bbp at different wavelengths using only Rrs as input parametersfor the IOCCG synthetic data set
is then calculated by linear regression between log [bbp()] and log
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The new model (LS-N)The new model (LS-N)
• Observations:
– The main pb is the retrieval of Kd() at different .
– The calculation of the parameter could be improved ( in the LP procedure does not always converge)
• Solutions:
– Use a NN approach to estimate Kd()
– A new way to account for to in the bbp vs. (Rrs, Kd) relationships
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• Use of artificial neural networks Multi-Layer Perceptron
– Purely empirical method– Universal approximator of any derivable function– Can handle “easily” noise
• Goal: Estimate of Kd(490) from Rrs
• First results:– Rrs between 412 and 670 nm– Log10(Kd(490)
Improvement of the Kd retrieval
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Dataset• Learning/testing datasets
– NOMAD database:• 337 set of (Rrs,Kd(490)
– IOCCG synthetical dataset:• 1500 set of (Rrs, Kd(490) • Three solar angles: 0°, 30°, 60°
• 65% of the entire dataset randomly taken for the learning phase (e.g., determination of the optimal configuration of the artificial neural networks)
• The rest of the dataset used for the validation phase
• Comparison of the results with:– Mueller (2000, 2005)– Werdell, 2009– Morel et al., 2007
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Validation Kd(490)
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Mueller 2000
Mueller 2005
Werdell 2009
Morel et al. 2007
NN
RMS 0.320 0.320 0.44 0.151 0.148
Relative error (%)
28.63 27.17 24.00 36.00 16.82
Adp (IOCCG, 2005)
0.612 0.582 0.413 0.956 0.274
Slope 0.36 0.35 0.98 0.51 0.80
Intercept 0.08 0.09 0.008 0.018 0.04
R (%) 86.47 86.12 69.36 90.20 94.70
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New model with true KdLP’ model (IOCCG) with true Kd
Between the IOCCG version and the new model RMSE decreases from 0.031 to 0.02for bbp (and by a factor of 2.6 for atot-w)
Performance of the new model at 490 nm using the IOCCG synthetic data set.
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New model with previous Kd parameterizations
New model with Kd estimated fromNeural Network
The Kd-NN allows to decrease the RMSE of bbp and atot-w by a factor of 1.92and 1.6, respectively.
Performance of the new model at 490 nm using the IOCCG synthetic data set.
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New model with Kd estimated from Neural Networks on the NOMAD data set
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Conclusions and perspectives for Kd• On the used dataset:
– Net overall improvement of the estimation of the kd(490)
– Same quality for the very low values of Kd(490), i.e. < 0.01
– Great improvement for the greater values, especially for very turbid waters (Kd(490) > 1)
• Need to test on another dataset
• Need to extend the learning dataset for very low values of Kd(490) (such as Biosope)
• Adding the algorithm of Lee (2005) in the comparison
• More tests of the useful inputs parameters
• Estimation of Kd at other wavelengths– One MLP for each Kd or one MLP for all Kd ???
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Conclusions of the new model
• Net improvement for the IOP inversion at 490 nm
• will be tested in next months at other wavelengths
• Impact on will be evaluated