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

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

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

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

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.

Performance of the LP’model at 490 nm using the IOCCG synthetic data set.

With true Kd With estimated Kd

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

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

• 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

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

Validation Kd(490)

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

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.

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.

New model with Kd estimated from Neural Networks on the NOMAD data set

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 ???

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