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IOCCG WG on Atmospheric correction over turbid waters

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

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IOCCG WG on Atmospheric correction over turbid waters

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

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

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

  4. 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

  5. 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

  6. 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: ρTOA? ρrc= ρa+ ρra+ t.ρw? 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

  7. 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

  8. 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 Rrs(NIR) • complicates BRDF (f/Q) corrections • saturation of observed radiances • anthropogenic emissions (NO2 absorption)  Need for accurate data processing algorithms

  9. Rationale (1/2) • Coastal waters more and more investigated in oceancolor • But more challengingthan 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 Rrs(NIR) • complicates BRDF (f/Q) corrections • saturation of observed radiances • anthropogenic emissions (NO2 absorption)  Need for accurate data processingalgorithms

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

  11. 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

  12. 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

  13. Which algorithms? 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. 2000 • use shortwave infrared bands e.g., Wang & Shi 2007 • correct/model the non-negligible Rrs(NIR) Lavender et al. 2005 MERIS Bailey et al. 2010 used in SeaWiFS Reprocessing 2010 Shanmugam, 2012 any sensor Wang et al. 2012 GOCI • 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

  14. 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

  15. 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?

  16. 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 ?

  17. 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?

  18. 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?

  19. Evaluation of AC • Can round robin lead to improvements? • Whatcanwelearn? • Drawbacks and advantages • Limitations • Sensitivitystudies • Fixedaerosols Variation/change of the bio-optical model • Fixed bio-optical model  Variation/change of the aerosolmodels • Uncertainties propagation and budget on the hypothesis • Ruddick et al. (2000) • Neukermans et al., (2012) • Bayseianstatistics for NN (Aires et al., 2004a, 2004b, 2004c) • Uncertainties on the NN parameters (weights) • Uncertainties on the outputs

  20. 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

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