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H. Loisel, C. Jamet, and D. Dessailly

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.

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H. Loisel, C. Jamet, and D. Dessailly

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  1. Recent advances for the inversion of the particulate backscattering coefficient at different wavelengths H. Loisel, C. Jamet, and D. Dessailly

  2. 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

  3. Advantages 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. The LS’s Model for in situ and RS applications h = bw/b

  4. Limitation for RS applications Use R(0-) instead of Rrs Kd is not measured from remote sensing but estimated from Rrs Original equations The LS’s Model for in situ and RS applications a = (- 0.83 + 5.34 h - 12.26 h2) + mw(1.013 - 4.124 h + 8.088 h2) d = (0.871 + 0.4 h - 1.83 h2)

  5. The LP’ (IOCCG, 2006): improvements of the first version of the model for remote sensing applications: • 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 h-b/a combinations at any given wavelengths used by actual ocean color sensors • We used some new parameterisations between <Kd(l)>1 and remote sensing ratios for each wavelength.

  6. Performance of the LP’model at 490 nm using the IOCCG synthetic data set. With true Kd With estimated Kd

  7. bbp at different wavelengths using only Rrs as input parameters for the IOCCG synthetic data set rmse = 0.123 slope = 0.902 intercept = -0.173 r2 = 0.924 slope = 0.973 intercept = -0.028 r2 = 0.934 rmse = 0.140 slope = 0.935 intercept = -0.114 r2 = 0.917 rmse =0.138 gis then calculated by linear regression between log [bbp(l)] and logl

  8. Observations: The main pb is the retrieval of Kd(l) at different l. The calculation of the h parameter could be improved (h in the LP procedure does not always converge) Solutions: Use a NN approach to estimate Kd(l) A new way to account for to h in the bbp vs. (Rrs, Kd) relationships The new model (LS-N)

  9. Improvement of the Kd retrieval • 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)

  10. 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

  11. Validation Kd(490)

  12. Performance of the new model at 490 nm using the IOCCG synthetic data set. LP’ model (IOCCG) with true Kd New model with true Kd Between the IOCCG version and the new model RMSE decreases from 0.031 to 0.02 for bbp (and by a factor of 2.6 for atot-w)

  13. 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 from Neural Network The Kd-NN allows to decrease the RMSE of bbp and atot-w by a factor of 1.92 and 1.6, respectively.

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

  15. 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 ???

  16. 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 g will be evaluated

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