Workshop on atmospheric correction over coastal waters: uncertainties and perspectives

1. Workshop on atmospheric correction over coastal waters: uncertainties and perspectives Laboratoire d’Océanologie et de Géosciences Wimereux, France 13-14 June, 2012

2. Why a workshop? • 15 years of continuous ocean satellite data: • ~13 years for SeaWiFS • ~10 years for MERIS • ~10 years of MODIS-AQUA (and still counting !!!) • Several AC for coastal waters are developed for the past decade: • SeaWiFS: Stumpf/Bailey; Hu; Kuchinke; Lavender; Ruddick: Shanmugam • MODIS-A: Stumpf/Bailey; Ruddick; Schroeder; Shanmugam; SWIR • MERIS: Moore; Brajard; Doerffer; Schroeder; Steinmetz • Complement of the IOCCG report #10: «  Atmospheric Correction for Remotely-Sensed Ocean-Colour Products » (Wang, 2010)  Update (could also be an update of IOCCG report #3) • Goal: understand where differences come from using in-situ and theoretical data to develop (a) new AC

3. Outlines of the workshop • Day 1: • 15 oral presentations • Goal: State-of-the-art + Direction of research  Food for day 2 • Day 2: • Round table/Discussion • Round robin exercise • Is it necessary? • Datasets • How to evaluate? • Which sensors? • Only nLw (but also AOPs)? • Sensitivities studies (hypothesis, environmental factors) • New IOCCG WG?

4. MREN (Université du Littoral) + CNRS Station Marine (Lille 1 Univ.) A research laboratory involving: • CNRS (National Center for Scientific Research • University of Lille 1 • Littoral University - ULCO Activities : • Research • Teaching • Housing (studients, researchers) • Observation Natural and preserved seaside

5. Laboratory of Oceanology and Geosciences • Multi-disciplinary laboratory • Topic of study: marine environment, mainly coastal areas • Object: Eastern English Channel, Manche orientale, highly tidal seas, others coastal seas • Biologists, physicists, biogeochimists, geomorphologists, geologists

6. Laboratory of Oceanology and Geosciences • 55 permanent positions: • 33 scientists (full time and assistant Prof/Professor) • 22 staffs (engineers, technicians and admin.) • 38 Non-permanent positions: • 15 post-docs • 23 PhD students

7. Laboratory of Oceanology and Geosciences • 5 research groups: • Plankton Ecology (bacterioplankton, phyto-,zooplankton, biological diversity, fluxes, food webs) • Benthic ecosystems and interface processes (shallow interdital and subtidal zones) • Biodiversity and climate (biodiversity, global change and natural fluctuations • Coastal morphodynamics (hydrodynamical, aerodynamical and sedimentary processes • Physical oceanography, transport and remote sensing (hydrodynamics, turbulence, remote sensing)

8. Main objective of the Physical oceanography, transport and remote sensing team Identify, prioritize, and understand the physical processes responsible for the spatio-temporal variability of the biogeochemical components of coastal areas Radiative transfer and dynamical modelisation Laboratory and in situ experimental approaches Satellite observations

9. Research topics Topic 1:Methodological, algorithms, and instrumental development. Topic 2:Biogeochemical variability analysis of the global and coastal oceans from in situ and satellite time series. Topic 3:Bio-optical and dynamical coupling studies for the analysis of the dynamic of suspended marine particles in coastal waters.

10. T1:Methodological, algorithms, and instrumental development Development and evaluation of atmospheric correction algorithms Neuro-Variational inversion: iterative approach for open ocean waters, absorbing aerosols and Coastal waters (Jamet et al., 2005; Brajard et al., 2006a, 2006b, 2008) Estimation of aerosol optical properties using neural networks inversion (Jamet et al., 2004) from SeaWiFS images

11. T1:Methodological, algorithms, and instrumental development Development of inverse methods to retrieve the absorption and the backscattering coefficients from ocean color remote sensing 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) Loisel and Stramski, 2000; Loisel et al., 2001

12. T1:Methodological, algorithms, and instrumental development Development of inverse methods to retrieve the absorption and the backscattering coefficients from ocean color remote sensing Validation with LOG(NN) Kd Validation with NASA Kd

13. T1:Methodological, algorithms, and instrumental development A Cloud Mask algorithm for coastal areas • Classical approach: • - Fixed threshold on the Rayleigh corrected TOA reflectance in the NIR (SeaWiFS) • Test on the spatial variability (POLDER, PARASOL, MODIS) • New method: Now included in SeaDAS K. Nordkvist, H. Loisel, L. Dufôret-Gaurier, Optics Express, 2009

14. T1:Methodological, algorithms, and instrumental development A new empirical parametrisation of Kd from Rrs using neural network inversion COASTCOLOU Round-Robin inter-comparison using simulated data

15. T1:Methodological, algorithms, and instrumental development A new empirical parametrisation of Kd from Rrs using neural network inversion Comparion using COASTLOOC in-situ datasets for SeaWiFS wavelengths (Jamet et al., in revision)

16. T2:Biogeochemical variability over the global ocean Inversion of bio-optical parameters from space and development of global climatology of phytoplankton species, particulate organic carbon, and particle size distribution proxy Some main focuses Validation of global biogeochemical models Analysis of the different spatio-temporal patterns in terms of biogeochemical processes Analysis of the inter-annual and decanal variability of the global ocean Loisel et al., 2002; Alvain et al., GBC 2008; Loisel et al., JGR 2006; Dufôret et al. DSR 2010

17. T3:Dynamic of suspended marine particles in coastal waters Seasonal and inter-annual (2002-2010) variability of the suspended particulate matter as retrieved from satellite ocean color sensor over the French Guiana coastal waters. Relative contribution of the irregular component to the variance of SPM detected from the X-11 decomposition procedure. Great variations probably associated with the presence of rings generated by the retroflection (Frantatoni et Glickson, 2002) Significant monotonic trends (p<0.05) in SPM over the 8 years of MODIS data. changes in SPM correspond preferentially to the migration of the nearshore mud banks

18. Involvement in satellite missions POLDER2 on ADEOS2 (2003): In charge by CNES for the development and validation of bio-optical algorithms MERIS: Involved in the validation tasks, implementation of our inverse algorithmes in ODESA SeaWiFS/MODIS: Implementation of our cloud mask in SeaWiFS, members of different groups on the inter-comparison of inverse bio-optical algorithms and atmospheric corrections Grail (Lidar): Submitted in response to European Space Agency Research Announcement for ISS Experiments relevant to study of Global Climate Change (1064 nm and 532 nm): information on vertical distribution of canopy/vegetation biomass (Green Carbon) and vegetation/phytoplankton biomass in open ocean and coastal waters OCAPI (Ocean Colour Advanced Permanent Imager): hope it will flight one day (Level 0 at CNES), geosynchronous orbit, 250 m

19. The GlobCoast Project: a frame for future collaborations Estimation and analysis of the seasonal, inter-annual and decadal biogeochemical variability of the world’s coastal waters by remote sensing and their impacts on higher trophic levels LOG, HYGEOS, LEGOS, and GET (a 3 years project that just starts)

20. The GlobCoast Project: a frame for future collaborations Objectives 1-To generate and analyse the seasonal, inter-annual, and decadal changes of the global coastal waters in term of biogeochemical composition as revealed from satellite ocean colour observations. 2-To analyse the temporal variability of ocean colour products as a function of physical forcing parameters as derived from remote sensing, in situ measurements or modelling. This part will be preferentially performed over four highly contrasted areascovering a great variety of environmental, biological and bio-optical conditions encountered in coastal areas: the English Channel and the North Sea, some major coastal upwelling systems, the Vietnamese coastal waters, and the Amazon-influenced coast (mainly French Guiana). 3- To examine the potential relationships between the variability in the environment and the variance in the recruitment and stocks of higher trophic level organisms (fishes) over different selected coastal areas representative of different coastal systems: the English Channel and the North Sea, and some major coastal upwelling systems.

21. The GlobCoast Project: a frame for future collaborations Task 2: A new approach to deal with atmospheric corrections in coastal areas Adaptation of the optimization technique used in the POLYMER algorithm (Steinmetz et al., 2010) for coastal regions 3 days MODIS 3 days MERIS 3 days MERIS

22. Atmospheric Correction Algorithms • Three NIR ocean contribution removing/AC algorithms (Jamet et al., RSE, 2011) • Stumpf et al. (2003)/ Bailey et al., (2010) S03: • Based on Gordon and Wang atmospheric correction (GW94) • SeaWiFS/MODIS standard algorithm • Iterative process • Bio-optical model used to determine bb(670) • Ruddick et al. (2000) R00: • Based on Gordon and Wang atmospheric correction (GW94) • Spatial homogeneity of the Lw(NIR) and LA(NIR) ratios over the subscene of interest • : Ratio of Lw(NIR) cst = 1.72 • ε: Ratio of LA(NIR) determined for each subscene • Kuchinke et al. (2009) K09: • Spectral optimization algorithm • Junge aerosol models • GSM bio-optical model (Garver, 2002) • Atmosphere and ocean coupled

23. DATA • Satellite data: • (M)LAC SeaWiFS 1km at nadir  Processed with SeaDAS 6.1 (R2009) (Fu et al., 1998) • nLw(412865), (865), (510,865) • In situ data:AERONET-OC network (Zibordi et al., 2006, 2009) • Three sites • 7 λ centered at 412, 443, 531, 551, 667, 870 and 1020 nm

25. Results • Only turbid waters (Robinson et al., 2003): nLw(670)>0.186 • Comparison of the normalized water-leaving radiances nLwbetween 412 and 670 nm and of the aerosol optical properties (the Ångström coefficient (510,865) and the optical thickness (865)) # of matchups for each algorithm and each AERONET-OC site

29. Conclusions (1/3) • Comparison of 3 SeaWiFS Atmospheric Correction algorithms • SeaWiFS standard algorithm: best overall estimates • Ruddick algorithm: less accurate • Kuchinke: good estimates for short wavelengths

30. Conclusions (2/3) • Sensitivity tests on assumptions of each algorithm • Ruddick: High impact of a bias of the value of the aerosol ratio ε • Stumpf R2009: Low impact of the new aerosol models for nLw; Moderate to High impact of the new definition of bb(670) • Kuchinke: High impact of the Junge aerosol models; “high impact” of the bio-optical parameters in GSM  need to tune the bio-optical models

31. Conclusions (3/3) • Time processing: • Standard algorithm: fastest algorithm • Kuchinke: very time consuming (as any optimization technique) • Ruddick: twice slower than standard algorithm (need to process two times the same image)