Optical Remote Sensing Laboratory, The City College of New York

1. Neural network approach for the derivation of chlorophyll concentration from ocean colorIoannis Ioannou(Presenter),Robert Foster, Alex Gilerson, Barry Gross, Fred Moshary and Sam Ahmed Optical Remote Sensing Laboratory, The City College of New York

2. Outline • Background • Neural network(NN) inversion algorithm for retrieval of IOP • NN algorithm for estimation of [Chl] • Application to satellite data • Summary

3. Outline • Background • Neural network(NN) inversion algorithm for retrieval of IOP • NN algorithm for estimation of [Chl] • Application to satellite data • Summary

4. Background- Ocean color remote sensing Assuming that other effects have been removed Algorithms relate measured radiance to IOPs IOPs(constituent absorption and scattering characteristics) Kirk (1974)

5. Algorithms Physical Parameters (IOPs) Noise/ uncertainty Forward Model (Physics) Radiance measurement Aires et. al. 2001 • Localized inversion: • Minimize the difference (Distance) D between the • measurement and the forward model • by adjusting the physical parameters. • Localized inversion is time consuming and sometimes impossible to perform Estimate of the physical parameters Established algorithm in this perspective is the semi analytical algorithm (SAA) “Overstrained linear matrix inversion” Wang et al., 2005 2) Global inversion: Estimate a priori a global model for the inverse of the forward model(y-1). Global inversion gives near real time retrievals Established algorithms in this perspective are the GSM (Maritorena el al.,2002), QAA (Lee et al.,2002), MERIS Neural network (Doerffer & Schiller, 2007)

6. Outline • Background • Neural network(NN) inversion algorithm for retrieval of IOP • NN algorithm for estimation of [Chl] • Application to satellite data • Summary

7. Process • Develop a simulated dataset based on a bio-optical model with IOPs typical for both open ocean and coastal waters. • Use this dataset to train a neural network(NN) algorithm • Use this same dataset to initially validate the NN • Validate the algorithm on field observations

8. 3-Simulated Dataset Four component model consisting of [Chl], CDOM, [NAP] and pure water corrected for average temperature and salinity.

9. The Global distribution of the NASA bio-Optical Marine Algorithm Data set (NOMAD) Werdell & Bailey 2005 Measurements of Lw, Ed , aphy , adm , ag , bbp, Kd λ={405, 411, 443, 455, 465, 489, 510, 520, 530, 550, 555, 560, 565, 570, 590, 619, 625, 665, 670, 683}

10. Comparison of simulated data with NOMAD Our simulations cover all observable range of parameters represented in NOMAD

11. … continued; Comparison of simulated data with NOMAD Tests similar to IOCCG Report 5

12. Algorithm description for retrieval of IOPs Input to the neural networks is the log10 of above water Reflectance Rrs at the visible MODIS wavelengths 412, 443, 488, 531, 547 and 667nm NN-I, NN-II, NN-III are one layer neural networks with 6 neurons at the hidden layer and are trained based on the simulated dataset. Retrieved parameters apg:the particulate and dissolved absorption coefficient bbp: particulate backscattering coefficient aphy: phytoplankton absorption coefficient adm: non-phytoplankton particulate absorption coefficient ag: dissolvedabsorption coefficient Ioannou et al. 2011 & 2013

13. Statistics used to evaluate the performance of the neural network The root mean square error (log10 domain) is Then the linear percentage error, e, is

14. Performance of the neural network on the part of our simulated dataset that was not used in the training stage, with 20% uniform noise added at each Rrs. R2 =0.9863 RMSE=0.0886 e=0.2264 R2 =0.9866 RMSE=0.0823 e=0.2086 R2 =0.8956(0.9428) RMSE=0.2675(0.1937) e=0.8515(0.5620) R2 =0.8821(0.9399) RMSE=0.3770(0.2443) e=1.3820(0.7553) R2 =0.9750(0.9751) RMSE=0.1363(0.1366) e=0.3687(0.3698) R2 =0.9107(0.936) RMSE=0.2602(0.23) e=0.8203(0.693)

15. Performance of the neural network on NOMAD

16. Outline • Background • Neural network(NN) inversion algorithm for retrieval of IOP • NN algorithm for estimation of [Chl] • Application to satellite data • Summary

17. [Chl] Algorithms 1) OC3 Operational established algorithm for MODIS Input: log10Rrs at 412, 443, 488, 531, 547 and 667nm 2) Rrs NN (NN [Chl]) Derived from the same Rrs, using the NN algorithm discussed in the previous section 3) IOP+RrsNN (IOPNN [Chl]) NN [Chl] and IOPNN [Chl] are both one hidden layer NNs with 10 neurons at the hidden layer and are trained based on NOMAD

18. Training and testing of the [Chl] NN algorithm using the NOMAD Training 90% NOMAD Testing 10% NOMAD

19. Outline • Background • Neural network(NN) inversion algorithm for retrieval of IOP • NN algorithm for estimation of [Chl] • Application to satellite data • Summary

20. Sample Seasonal [Chl], Spring 2003 Algorithm Implementation on Satellite Data- OC3-[Chl], mg m-3

21. Sample Seasonal [Chl], Spring 2003 Algorithm Implementation on Satellite Data- NN-[Chl], mg m-3

22. Sample Seasonal [Chl], Spring 2003 Algorithm Implementation on Satellite Data- IOPNN[Chl], mg m-3

23. Global Distributions of [Chl], as derived from the three algorithms for Spring 2003

24. Global seasonal variation of [Chl]mg/m3 [Chl]mg/m3

25. Algorithm Implementation on Satellite Data- Percent difference of {IOPNN[Chl]}– {OC3[Chl]} Spring 2003

26. Outline • Background • Neural network(NN) inversion algorithm for retrieval of IOP • NN algorithm for estimation of [Chl] • Application to satellite data • Summary

27. Summary • Based on a 4 component model: pure water, [Chl], [NAP] and CDOM we created a bio-optical model to obtain ocean IOP for the whole range of typical open ocean and coastal water parameters. • These IOP were used as inputs into HYDROLIGHT 5 to simulate oceanic Rrs. • Simulated datasets were used to train a NN algorithm and test the accuracy of retrievals with noise. • NN method was used for the retrieval of the following products: bb (443),aph(443),adm(443) & ag(443). • NN retrievals were also compared with the field measurements of the NOMAD showing excellent agreement. • A NN algorithm was developed using the NOMAD and derives [Chl] from Rrs measurements and previously derived IOPs. • Retrievals were analyzed on satellite imagery depicting the seasonal variability of [Chl]

28. Acknowledgements • This work was partially supported by grants from ONR and NOAA. • We would also like to thank the NASA Ocean Biology Processing Group and all NOMAD contributors.