Inter-comparison of atmospheric correction over moderately to very turbid waters Cédric Jamet Kick-off meeting Wimereux, France May, 14, 2014

Rationale 15 years of continuous ocean satellite data: –~13 years for SeaWiFS –~10 years for MERIS –~10 years of MODIS-AQUA (and still counting) –VIIRS (2012-) –OLCI (2015-) –GOCI (2010- )  Possibility to study inter-annual and decennal variability of biogeochemical parameters in open but also in coastal waters

Rationale Need for accurate atmospheric correction algorithms  Need to look at the existing algorithms Several AC developed for the past 12 years  no assessment of differences Future ocean color sensors: high spatial resolution, high radiometric resolution, other wavelengths  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)

Courtesy of PJ Werdell no one uses the “black pixel assumption” anymore

SeaWIFS 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 b b (670) –Ruddick et al. (2000) R00: Based on Gordon and Wang atmospheric correction (GW94) Spatial homogeneity of the L w (NIR) and L A (NIR) ratios over the subscene of interest  : Ratio of L w (NIR) cst = 1.72 ε: Ratio of L A (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

ρ' w (λ VIS ) ρ w (λ red ) Chl a Black Pixel Assumption ρ w ( λ NIR ) = 0 Chl a based bio-optical model First guess in ρ w (λ) ρ w (λ NIR ) ρ a (λ)+ ρ ra (λ)= ρ rc (λ)- ρ w (λ) ρ rc =ρ t -ρ r -tρ wc -Tρ g Remove white caps, sun glint, ozone and Rayleigh scattering Modelled ρ w (λ NIR ) ρ w (λ) Second guess in ρ w (λ) Gordon and Wang, 1994 NIR modelling scheme ρ a (λ)+ ρ ra (λ)= ρ rc (λ)

SeaWIFS 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 b b (670) –Ruddick et al. (2000) R00: Based on Gordon and Wang atmospheric correction (GW94) Spatial homogeneity of the L w (NIR) and L A (NIR) ratios over the subscene of interest  : Ratio of L w (NIR) cst = 1.72 ε: Ratio of L A (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

Black Pixel Assumption ρ w ( λ NIR ) = 0 ε( λ NIR1, λ NIR2 )= [ ρ a (λ NIR1 )+ ρ ra (λ NIR1 )]/ [ ρ a (λ NIR2 )+ ρ ra (λ NIR2 )] ρ a (λ)+ ρ ra (λ)= ρ rc (λ)- ρ w (λ) ρ w (λ NIR ) ρ rc =ρ t -ρ r -tρ wc -Tρ g Remove white caps, sun glint, ozone and Rayleigh scattering ρ w (λ) First guess in ρ a (λ)+ ρ ra (λ) GordonandWang,1994 ρ' a (λ)+ ρ' ra (λ)= ρ rc (λ) a = ρ w (λ NIR1 )/ ρ w (λ NIR2 ) Spatial homogeneity in aerosols NIR similarity spectrum NIR modelling scheme

SeaWIFS 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 b b (670) –Ruddick et al. (2000) R00: Based on Gordon and Wang atmospheric correction (GW94) Spatial homogeneity of the L w (NIR) and L A (NIR) ratios over the subscene of interest  : Ratio of L w (NIR) cst = 1.72 ε: Ratio of L A (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

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

Results Only turbid waters (Robinson et al., 2003): nLw(670)>0.186 Comparison of the normalized water-leaving radiances nL w between 412 and 670 nm and of the aerosol optical properties (the Ångström coefficient  (510,865) and the optical thickness  (865)) MVCOAAOTCOVETOTALnL w (412) <0 S R Kuchinke # of matchups for each algorithm and each AERONET-OC site

Jamet et al., 2011

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

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 b b (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

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)

MODIS Atmospheric Correction Algorithms Four NIR ocean contribution removing/AC algorithms (Goyens et al., RSE, 2013) –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 b b (670) –Ruddick et al. (2000) R00: Based on Gordon and Wang atmospheric correction (GW94) Spatial homogeneity of the L w (NIR) and L A (NIR) ratios over the subscene of interest  : Ratio of L w (NIR) cst = 1.72 ε: Ratio of L A (NIR) determined for each subscene –Wang and Shi (2005, 2009): Based on GW94 SWIR bands for determination of aerosol models: black pixel assumption –Shroeder et al. (2007) NN direction inversion Coupled ocean-atmosphere

MODIS Atmospheric Correction Algorithms Four NIR ocean contribution removing/AC algorithms (Goyens et al., RSE, 2013) –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 b b (670) –Ruddick et al. (2000) R00: Based on Gordon and Wang atmospheric correction (GW94) Spatial homogeneity of the L w (NIR) and L A (NIR) ratios over the subscene of interest  : Ratio of L w (NIR) cst = 1.72 ε: Ratio of L A (NIR) determined for each subscene –Wang and Shi (2005, 2009): Based on GW94 SWIR bands for determination of aerosol models: black pixel assumption –Shroeder et al. (2007) NN direction inversion Coupled ocean-atmosphere

NIR/SWIR Switching algorithms based on Turbid Water Index T ind ~ ρ rc (748, 1240) GW94-based algorithm assuming ρ w (λ SWIR )=0 ρ rc =ρ t -ρ r -tρ wc -Tρ g Remove white caps, sun glint, ozone and Rayleigh scattering ρ w (λ) STD AC method SWIR AC method ~ Gordon andWang,1994

MODIS Atmospheric Correction Algorithms Four NIR ocean contribution removing/AC algorithms (Goyens et al., RSE, 2013) –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 b b (670) –Ruddick et al. (2000) R00: Based on Gordon and Wang atmospheric correction (GW94) Spatial homogeneity of the L w (NIR) and L A (NIR) ratios over the subscene of interest  : Ratio of L w (NIR) cst = 1.72 ε: Ratio of L A (NIR) determined for each subscene –Wang and Shi (2005, 2009): Based on GW94 SWIR bands for determination of aerosol models: black pixel assumption –Shroeder et al. (2007) NN direction inversion Coupled ocean-atmosphere

MVCO Approach Overview c. In situ data COVE HELSINKI AERONET-OC data

MVCO Approach Overview c. In situ data COVE HELSINKI AERONET-OC data Moderately turbid

Approach Overview c. In situ data In situ MUMM & LOG data TriOS RAMSES above and in water measurements COVE Moderately to extremely turbid

Approach Overview c. In situ data In situ MUMM & LOG data 20 spectra76 spectra 21 spectra93 spectra Hyperspectral TriOS ρ w (λ) LOG data

Approach Overview c. In situ data In situ MUMM & LOG data Hyperspectral TriOS ρ w (λ) MUMM data 131 spectra with moderately to extremely turbid waters

Approach Overview c. In situ data In situ MUMM & LOG data French Guiana 2012 RV Melville – South Atlantic 2011

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods a. Global Evaluation Goyens et al., (2013) [a] MUMM 211 AERONET-OC & LOG match-ups Overall the STD AC method performs the best ↑ spatial coverage but ↑ neg. L wn (λ) values with the MUMM AC method STD, MUMM and NIR-SWIR tend to underestimate L wn (λ) (bias between -26 and 3%) STD, MUMM and NIR-SWIR perform better in the green ( 30% RE) NN performs better in the blue (30% RE) and red (22% RE) and not as well in the green (up to 27% RE)

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods a. Global Evaluation MUMM 211 match-ups AERONET-OC & LOG) Overall the STD AC method performs the best ↑ spatial coverage but ↑ neg. L wn (λ) values with the MUMM AC method STD, MUMM and NIR-SWIR tend to underestimate L wn (λ) (bias between -26 and 3%) STD, MUMM and NIR-SWIR perform better in the green ( 30% RE) NN performs better in the blue (30% RE) and red (22% RE) and not as well in the green (up to 27% RE) Goyens et al., (2013) [a]

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods a. Global Evaluation MUMM 211 match-ups AERONET-OC & LOG) Overall the STD AC method performs the best ↑ spatial coverage but ↑ neg. L wn (λ) values with the MUMM AC method STD, MUMM and NIR-SWIR tend to underestimate L wn (λ) (bias between -26 and 3%) STD, MUMM and NIR-SWIR perform better in the green ( 30% RE) NN performs better in the blue (30% RE) and red (22% RE) and not as well in the green (up to 27% RE) Goyens et al., (2013) [a]

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods a. Global Evaluation MUMM 211 match-ups AERONET-OC & LOG) Overall the STD AC method performs the best ↑ spatial coverage but ↑ neg. L wn (λ) values with the MUMM AC method STD, MUMM and NIR-SWIR tend to underestimate L wn (λ) (bias between -26 and 3%) STD, MUMM and NIR-SWIR perform better in the green ( 30% RE) NN performs better in the blue (30% RE) and red (22% RE) and not as well in the green (up to 27% RE) Goyens et al., (2013) [a]

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods a. Global Evaluation MUMM 211 match-ups AERONET-OC & LOG) Overall the STD AC method performs the best ↑ spatial coverage but ↑ neg. L wn (λ) values with the MUMM AC method STD, MUMM and NIR-SWIR tend to underestimate L wn (λ) (bias between -26 and 3%) STD, MUMM and NIR-SWIR perform better in the green ( 30% RE) NN performs better in the blue (30% RE) and red (22% RE) and not as well in the green (up to 27% RE) Goyens et al., (2013) [a]

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods a. Global Evaluation MUMM 211 match-ups AERONET-OC & LOG) Overall the STD AC method performs the best ↑ spatial coverage but ↑ neg. L wn (λ) values with the MUMM AC method STD, MUMM and NIR-SWIR tend to underestimate L wn (λ) (bias between -26 and 3%) STD, MUMM and NIR-SWIR perform better in the green ( 30% RE) NN performs better in the blue (30% RE) and red (22% RE) and not as well in the green (up to 27% RE) Goyens et al., (2013) [a]

Goyens et al., 2013

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods b. Water class-specific validation Goyens et al., (2013) [a] Normalized LOG Rrs(λ) data spectra per class according to the classification scheme of Vantrepotte et al. (2012) Detrital/mineral particles & low phytoplankton Phytoplankton CDOM Goyens et al., (2013) [a]

Goyens et al., 2013; Vantrepotte et al., 2012 (CDOM & phyto) (detrital & mineral) (phyto)

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods b. Water class-specific validation Goyens et al., (2013) [a] Detrital/mineral particles & low phytoplankton - NN AC method shows the best performances (RE from 16% to 27% and bias from -7% to 5%) - Among the GW94-based AC methods, the MUMM approach results in the best performances (RE from 20% to 43% and bias from -42% to -19%) Goyens et al., (2013) [a]

I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods b. Water class-specific validation Goyens et al., (2013) [a] - Overall highest RE (up to 73%) and bias (up to -26%) are encountered over CDOM dominated waters - STD AC method shows the best performances (RE from 14% to 53% and bias from -16% to 14%) CDOM Goyens et al., (2013) [a]

- Overall the STD AC method performs the best - The MUMM AC method ensures a large spatial coverage (but retrieves more neg. ρ w (λ) values) - For water masses optically dominated by detrital/mineral particles the MUMM AC method performs better In situ-satellite match-up pairs In situ ρ w (λ) vs. satellite ρ w (λ) Global validation Water class- specific validation I. Evaluation and comparison of existing turbid water AC methods Determine best AC method(s) to improve ! I. Evaluationcomparison I. Evaluation and comparison of turbid water AC methods c. Conclusion

Conclusions Several methods exist today  Difficult to estimate which one is the best All methods have advantages and limitations Not sure accuracy is reached for short visible wavelengths Still work to do or the actual accuracy is enough for bio- optical applications???

My participation Chairman: –Management of the WG –Editor of the report –Organizer of annual and teleconf meetings Processing datasets with Goyens et al. (2014) AC Collection of in-situ datasets Full analysis of the outputs of the AC –Match-ups –Transects –Sensitivities studies –Time series

Your participation Developers provide their algorithm (+ process datasets) How do you want to participate? –K. Stamnes: algorithm + RT model for atmosphere –X. He: algorithm + RT model for atmosphere –S. Sterckx: adjacency effects comparison + extreme turbid waters in-situ dataset –P. Shanmugam: algorithm + in-situ dataset in Indian Ocean –J. Brajard: algorithm –S. Bailey: NASA in-situ datasets + implementation of AC in SeaDAS + algorithm –K. Ruddick: link with IOCCG WG + in-situ datasets –T. Schroeder: algorithm + in-situ datasets around Australia –M. Wang: experience from report #10 + algorithms

Why a new IOCCG WG? 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 And in coastal waters?? Need for guidances on using the already developed AC  Purpose of this new WG

Summary Goal: Inter-comparison and evaluation of existing AC algorithms over turbid/coastal waters  Understanding retrievals differences between 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

Questions How well do the aerosols need to be retrieved? Does the technique matter? How good is the extrapolation from SWIR to NIR to VIS? How to improve AC with the historic wavelengths? –Better bio-optical models –New contrains (such as relationships between Rrs, cf Poster of Goyens et al.) –Regional AC? Do we really need extra wavelengths in NIR/SWIR? What info can offer the future satellite sensors?

Evaluation of AC Can round robin lead to improvements? –What can we learn? Range of validity 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) Bayseian statistics for NN (Aires et al., 2004a, 2004b, 2004c) –Uncertainties on the NN parameters (weights) –Uncertainties on the outputs Others ?

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

Classification of Lw spectra per water type - 4 water type classes defined by Vantrepotte et al. (2012) - Novelty detection technique: Assigns each spectra to one of the 4 water type classes using the Mahalanobis distance METHODS Focus on turbid waters only ! Distinguish classes based on normalized reflectance spectra D'Alimonte et al. (2003)

Evaluation of the algorithms as a function of the water type RESULTS We don't have any mathup for Class 3 (very turbid water masses)! Goyens et al., 2013

Rationale 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 –Land 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 sensor saturation –shallow waters  Need for accurate data processing algorithms

Rationale 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 –Land 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 sensor saturation –shallow waters  Need for accurate data processing algorithms Robert Frouin

Rationale 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 –Land 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 sensor saturation –shallow waters  Need for accurate data processing algorithms Sean Bailey

Rationale 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 –Land 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 sensor saturation –shallow waters  Need for accurate data processing algorithms

ρ t : total (measured) ρ r : Rayleigh (known) ρ a : aerosols (unknown) ρ ra : rayleigh-aerosols (unknown) ρ wc : whitecaps (modelled/wind) ρ w : water (unknown, 10% of ρ t ) ρ g : glitter (masked or modelled Objectives: 5% for absolute radiances and 2% for relative reflectances Rationale of atmospheric correction

ρ t : total (measured) ρ r : Rayleigh (known) ρ a : aerosols (unknown) ρ ra : rayleigh-aerosols (unknown) ρ wc : whitecaps (modelled/wind) ρ w : water (unknown, 10% of ρ t ) ρ g : glitter (masked or modelled) Objectives: 5% for absolute radiances and 2% for relative reflectances Rationale of atmospheric correction

Classic Black Pixel Assumption (1/2) NIR bands 670nm765nm865nm Hypothesis: absorbing ocean   w  negligeable  t ( )-  r ( )=  ra (  a (  A ( )

Classic Black Pixel Assumption (1/2) NIR bands 670nm765nm865nm Hypothesis: absorbing ocean   w  negligeable  t ( )-  r ( )=  ra (  a (  A ( ) calculate aerosol ratios,  :  (748,869) ~  (,869) ~  a (869)  a (748)  a (869)  a ( )

Classic Black Pixel Assumption (1/2) Determination of aerosols models  Calculation of ρ A and t NIR bands 670nm765nm865nm Hypothesis: absorbing ocean   w  negligeable  t ( )-  r ( )=  ra (  a (  A ( )

Classic Black Pixel Assumption (2/2) 412nm 443nm 490nm510nm555nm Visible bands,  t ( )-  r ( )-  A ( ))/t( ) =  w ( )

Courtesy of PJ Werdell no one uses the “black pixel assumption” anymore

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 use of UV bands He et al., 2012 correct/model the non-negligible R rs (NIR) Lavender et al. 2005MERIS Bailey et al used in SeaWiFS Reprocessing 2010 Moore et al MERIS/OLCI Wang et al GOCI use a coupled ocean-atmosphere optimization/inversion e.g., Chomko & Gordon 2001; Stamnes et al. 2003; Jamet et al., 2005; Ahn and Shanmugam, 2007; Doerffer & Schiller, 2008; Kuchinke et al. 2009; Schroeder et al., 2007; Steinmetz et al., 2010; Brajard et al., 2012 Approaches to account for R rs (NIR) > 0 sr -1 overlap Courtesy of Jeremy Werdell

Evaluation 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) Only few papers on round-robin

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 use of UV bands He et al., 2012 correct/model the non-negligible R rs (NIR) Lavender et al. 2005MERIS Bailey et al. 2010used in SeaWiFS Reprocessing 2010 Moore & Lavender, 1999 MERIS/OLCI Wang et al. 2012GOCI use a coupled ocean-atmosphere optimization/inversion e.g., Chomko & Gordon 2001; Stamnes et al. 2003; Jamet et al., 2005, Ahn and Shanmugam, 2007; Doerffer & Schiller, 2008; Kuchinke et al. 2009; Schroeder et al., 2007; Steinmetz et al., 2010; Brajard et al., 2012 Approaches to account for R rs (NIR) > 0 sr -1 overlap Courtesy of Jeremy Werdell

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 use of UV bands He et al., 2012 correct/model the non-negligible R rs (NIR) Lavender et al. 2005MERIS Bailey et al. 2010used in SeaWiFS Reprocessing 2010 Moore & Lavender 1999, MERIS/OLCI Wang et al. 2012GOCI use a coupled ocean-atmosphere optimization/inversion e.g., Chomko & Gordon 2001; Stamnes et al. 2003; Jamet et al., 2005; Ahn and Shanmugam, 2007; Doerrfer & Schiller, 2008; Kuchinke et al. 2009; Schroeder et al., 2007; Steinmetz et al., 2010; Brajard et al., 2012 Approaches to account for R rs (NIR) > 0 sr -1 overlap Courtesy of Jeremy Werdell

Kratzer et al., 2010

He et al., 2012 USE OF UV BANDS