IOCCG WG on Atmospheric correction over turbid waters C. Jamet Kick-off meeting Wimereux, France May, 14, 2014

Actual members Confirmed participation: –Sean Bailey, NASA, USA –Julien Brajard, LOCEAN, France –Xiangqiang He, SIO, China –Cédric Jamet (Chairman), LOG ULCO/CNRS, France –Els Knaeps, VITO, Belgium –Kevin Ruddick, MUMM, Belgium –Palanasamy Shanmugam, ITT, India –Thomas Schroeder, CSIRO, Australia –Knut Stamnes, Stevens Institute of Technology, USA –Menghua Wang, NOAA, USA

Actual members Confirmed participation: –Sean Bailey, NASA, USA –Julien Brajard, LOCEAN, France –Xiangqiang He, SIO, China –Cédric Jamet (Chairman), LOG ULCO/CNRS, France –Els Knaeps, VITO, Belgium –Kevin Ruddick, MUMM, Belgium –Palanasamy Shanmugam, ITT, India –Thomas Schroeder, CSIRO, Australia –Knut Stamnes, Stevens Institute of Technology, USA –Menghua Wang, NOAA, USA

Wednesday, 14, May, 2014: 9:00-9:30: Welcome (C. Jamet) 9:30-9:45: Why a new IOCCG WG? Purpose, goals of the WG and the kick-off meeting 9:45-10:15: Inter-comparison of atmospheric correction in moderate turbid waters for SeaWiFS and MODIS-AQUA (C. Jamet) 10:15-10:45: Atmospheric correction using NeuroVaria, (J. Brajard) 10:45-11:00: Coffee break 11:00-11:30: Contribution from Stevens Institute of Technology (K. Stamnes) 11:30-12:00: Sunglint and aerosol correction algorithms for ocean colour sensors: Preliminary results (P. Shanmugam) 12:00-12:30: Contribution from CSIRO (T. Schroeder) 12:30-14:00: Lunch

Wednesday, 14, May, 2014: 14:00-14:30: The use of NIR and SWIR wavelengths in the atmospheric and environment correction (S. Sterckx) 14:30-15:00: Atmospheric correction using the ultraviolet wavelength for highly turbid waters (X. He) 15:00-17:00: Discussions Role of each participant Scope of the WG Previous experience on round-robins Lessons learnt from IOCCG report #10, CCI OC, What type of algorithms? Which sensors? Which datasets? In-situ? Simulated? Satellite? Where? Schedule of the WG? Report in 2 years? 17:00: Adjourn

Thursday, 15, May: 9:00-10:30: Discussions Simulated datasets: useful? which aerosol models? Which RTE code for atmosphere and ocean? How to define IOPs (link with IOCCG WG K. Ruddick). What to simulate: ρ TOA ? ρ rc = ρ a + ρ ra + t.ρ w ? Multi-scattering? Which definition of Rrs? How to compare? Match-ups? Water-type classification Transects Error propagation Sensitivities studies: fixed aerosol/bio-optical model 10:30-11:00: Coffee break 11:00-12:30: Discussions Sensitivities studies: fixed aerosol/bio-optical model How to compare? Match-ups? Water-type classification Transects Error propagation Influence of observation angles ???? Adjacency effects Other suggestions 12:30-14:00: Lunch

Thursday, 15, May: 14:00-15:30: Discussions One day workshop at Ocean Optics (Sunday, 25, October): 6- month progress Schedule of the WG? Report in 2 years? Management of the WG 15:30: Adjourn

Rationale (1/2) Coastal waters more and more investigated in ocean color But more challenging than open ocean waters: –temporal & spatial variability satellite sensor resolution satellite repeat frequency validity of ancillary data (SST, wind) resolution requirements & binning options –Straylight contamination (adjacency effects) –non-maritime aerosols (dust, pollution) region-specific models required? absorbing aerosols –suspended sediments & CDOM complicates estimation of R rs (NIR) complicates BRDF (f/Q) corrections saturation of observed radiances –anthropogenic emissions (NO 2 absorption)  Need for accurate data processing algorithms

Rationale (1/2) Coastal waters more and more investigated in ocean color But more challenging than open ocean waters: –temporal & spatial variability satellite sensor resolution satellite repeat frequency validity of ancillary data (SST, wind) resolution requirements & binning options –Straylight contamination (adjacency effects) –non-maritime aerosols (dust, pollution) region-specific models required? absorbing aerosols –suspended sediments & CDOM complicates estimation of R rs (NIR) complicates BRDF (f/Q) corrections saturation of observed radiances –anthropogenic emissions (NO 2 absorption)  Need for accurate data processing algorithms

Other issues This WG: only on nLw(NIR) ≠ 0 Other issues not adressed One dedicated chapter –Sun glint –Adjacency effects

Why a new WG? (2/2) 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) This WG focused mainly on open ocean waters Goal: understand where differences come from using in-situ and theoretical data to develop (a) new AC (b) provide guidances to use the different AC

Rationale (2/2)  Need for accurate data processing algorithms Several AC developed for the past 12 years  no assessment of differences Future ocean color sensors: high spatial resolution, high radiometric resolution + long time series now  Highest possibility to study coastal waters Need for guidances on using the already developed AC (see number of requests on the forum of oceancolor.gsfc.nasa.gov)  Purpose of this new WG  Need to inter-compare existing AC to understand differences and advantages and how their own assumptions impact the quality of the retrievals

many approaches exist, here are a few examples: assign aerosols (  ) and/or water contributions (Rrs(NIR)) e.g., Hu et al. 2000, Ruddick et al use shortwave infrared bands e.g., Wang & Shi 2007 correct/model the non-negligible R rs (NIR) Lavender et al. 2005MERIS Bailey et al. 2010used in SeaWiFS Reprocessing 2010 Shanmugam, 2012 any sensor Wang et al. 2012GOCI use a coupled ocean-atmosphere optimization e.g., Moore et al., 1999; Chomko & Gordon 2001, Stamnes et al. 2003, Jamet et al., 2005, Brajard et al., 2006a, b, 2008, 2012; Ahn and Shanmugam, 2007; Kuchinke et al. 2009; Steinmetz et al., 2010; Other e.g., Chen et al., 2014; Doerrfer et al., 2007; He et al., 2013; Mao et al., 2013, 2014; Schroeder et al., 2007; Singh and Shanmugam, 2014 Which algorithms?

Past Evaluation of AC already exists in journals: –SeaWiFS: Zibordi et al. (2006, 2009); Banzon et al., 2009; Jamet et al. (2011); –MODIS-AQUA: Zibordi et al. (2006, 2009); Wang et al. (2009), ;Werdell et al., 2010; Goyens et al. (2013) –MERIS: Zibordi et al. (2006, 2009); Cui et al. (2010); Kratzer et al. (2010); Melin et al. (2011) BUT most of the time only about one specific AC (with eventually comparisons with the official AC) + regional assessments Only few papers on comparison of >3 AC

Now and Future What can we do with all those algorithms? Some implemented in SeaDAS or BEAM (or ODESA) What is the message for end-users studying coastal waters? Are their accuracies enough/satisfying? Use of a round-robin on AC over coastal waters? Do we need improvements? Round-robin for bio-optical algorithms (new IOCCG WG chaired by K. Ruddick)  How to take into accounts the uncertainties on Rrs?

Evaluation of AC Can round robin lead to improvements? –What can we learn? Range of validity and advantages Limitations (water type?) –Sensitivity studies Fixed aerosols  Variation/change of the bio-optical model Fixed bio-optical model  Variation/change of the aerosol models –Uncertainties propagation and budget on the hypothesis Ruddick et al. (2000) Bayseian statistics for NN (Aires et al., 2004a, 2004b, 2004c) –Uncertainties on the NN parameters (weights) –Uncertainties on the outputs Others ?

How to do the round-robin Ideally: –Having all algorithms in one software (such as SeaDAS)  Same data processing –Comparison: Match-up exercise Transect Time series –Sensitivity studies –Synthetical dataset? Using new or existing datasets? –One given sensor?

How to do the round-robin Do we compare apple with pears? Definition of Rrs Vicarious calibration? Round-robin from the point of view of an end- user or of a developer?

Evaluation of AC Can round robin lead to improvements? –What can we learn? Drawbacks and advantages Limitations –Sensitivity studies Fixed aerosols  Variation/change of the bio-optical model Fixed bio-optical model  Variation/change of the aerosol models –Uncertainties propagation and budget on the hypothesis Ruddick et al. (2000) Neukermans et al., (2012) Bayseian statistics for NN (Aires et al., 2004a, 2004b, 2004c) –Uncertainties on the NN parameters (weights) –Uncertainties on the outputs

Summary Goal: Inter-comparison and evaluation of existing AC algorithms over turbid waters  Understanding retrievals differences algorithms Challenge: to understand the advantages and limitations of each algorithm and their performance under certain atmospheric and oceanic conditions Only focus on AC algorithms that deal with a non-zero NIR water-leaving radiances. High demand for AC guidelines Outputs timely  Guidances on the use of AC over turbid waters  Recommendations for improving and selecting the optimal AC Not a sensor-oriented exercise