Inter-comparison of atmospheric correction over moderately to very turbid waters Cédric Jamet...
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Transcript of Inter-comparison of atmospheric correction over moderately to very turbid waters Cédric Jamet...
Inter-comparison of atmospheric correction over
moderately to very turbid watersCédric Jamet
Kick-off meetingWimereux, 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
RationaleNeed 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 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
ρ'w(λ
VIS)
ρw(λ
red)
Chla
Black Pixel Assumption ρw(λ
NIR) = 0
Chla based bio-optical model
First guess in ρw(λ)
ρw(λ
NIR)
ρa(λ)+ρ
ra(λ)=ρ
rc(λ)-ρ
w(λ)
ρrc=ρ
t-ρ
r-tρ
wc-Tρ
g
Remove white caps, sun glint, ozone and Rayleigh scattering
Modelled ρw(λ
NIR)
ρw(λ)Second guess in ρ
w(λ)
Gordon and Gordon and Wang, 1994Wang, 1994
NIR modelling NIR modelling schemescheme
ρa(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 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
Black Pixel Assumption ρw(λ
NIR) = 0
ε(λNIR1
,λNIR2
)=
[ρa(λ
NIR1)+ρ
ra(λ
NIR1)]/[ρ
a(λ
NIR2)+ρ
ra(λ
NIR2)]
ρa(λ)+ρ
ra(λ)=ρ
rc(λ)-ρ
w(λ)
ρw(λ
NIR)
ρrc=ρ
t-ρ
r-tρ
wc-Tρ
g
Remove white caps, sun glint, ozone and Rayleigh scattering
ρw(λ)
First guess in ρa(λ)+ρ
ra(λ)
Gordon Gordon and and
Wang, Wang, 19941994
ρ'a(λ)+ρ'
ra(λ)=ρ
rc(λ)
a =ρw(λ
NIR1)/ρ
w(λ
NIR2)
Spatial homogeneity in aerosolsNIR similarity spectrum
NIR modelling NIR modelling schemescheme
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 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
DATA• Satellite data:
– (M)LAC SeaWiFS 1km at nadir Processed with SeaDAS 6.1 (R2009) (Fu et al., 1998)
– nLw(412865), (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
nLw between 412 and 670 nm and of the aerosol optical properties (the Ångström coefficient (510,865) and the optical thickness (865))
MVCO AAOT COVE TOTAL nLw(412) <0
S03 20 163 18 201 7
R00 19 129 17 165 6
Kuchinke 13 134 13 160 0
# 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 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
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 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
– 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 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
– 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/SWIRSwitching algorithms based on
Turbid Water Index
Tind
~ ρrc(748, 1240)
GW94-based algorithm
assuming ρw(λ
SWIR)=0
ρrc=ρ
t-ρ
r-tρ
wc-Tρ
g
Remove white caps, sun glint, ozone and Rayleigh scattering
ρw(λ)
STD AC method
SWIR AC method
~ Gordon ~ Gordon and and
Wang, Wang, 1994 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 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
– 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 OverviewApproach Overview
c. c. In situ In situ datadata
COVE
HELSINKI
• AERONET-OC data
MVCO
Approach OverviewApproach Overview
c. c. In situ In situ datadata
COVE
HELSINKI
• AERONET-OC data Moderately turbidModerately turbid
Approach OverviewApproach Overview
c. c. In situ In situ datadata• In situ MUMM & LOG data
TriOS RAMSES above and in water measurementsTriOS RAMSES above and in water measurementsCOVE
ModeratelyModerately to to eextremely turbidxtremely turbid
Approach OverviewApproach Overview
c. c. In situ In situ datadata• In situ MUMM & LOG data
20 spectra 76 spectra
21 spectra 93 spectra
Hyperspectral TriOS ρHyperspectral TriOS ρww(λ) LOG data(λ) LOG data
Approach OverviewApproach Overview
c. c. In situ In situ datadata• In situ MUMM & LOG data
Hyperspectral TriOS ρHyperspectral TriOS ρww(λ) MUMM data(λ) MUMM data
131 spectra with moderately to extremely turbid waters
Approach OverviewApproach Overview
c. c. In situ In situ datadata• In situ MUMM & LOG data
French Guiana 2012
RV Melville – South Atlantic 2011
I. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
a. Global Evaluationa. 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.
Lwn
(λ) values with the MUMM AC
method STD, MUMM and NIR-SWIR tend
to underestimate Lwn
(λ) (bias
between -26 and 3%) STD, MUMM and NIR-SWIR
perform better in the green (< 20% RE), not as well in the blue & red (> 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
a. Global Evaluationa. Global EvaluationMUMM
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 Lwn
(λ) (bias
between -26 and 3%) STD, MUMM and NIR-SWIR
perform better in the green (< 20% RE), not as well in the blue & red (> 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
a. Global Evaluationa. Global EvaluationMUMM
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 Lwn
(λ) (bias
between -26 and 3%) STD, MUMM and NIR-SWIR
perform better in the green (< 20% RE), not as well in the blue & red (> 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
a. Global Evaluationa. Global EvaluationMUMM
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 (< 20% RE), not as well in the blue & red (> 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
a. Global Evaluationa. Global EvaluationMUMM
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 Lwn
(λ) (bias
between -26 and 3%) STD, MUMM and NIR-SWIR
perform better in the green (< 20% RE), not as well in the blue & red (> 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
a. Global Evaluationa. Global EvaluationMUMM
211 match-ups AERONET-OC & LOG)
Overall the STD AC method performs the best
↑ spatial coverage but ↑ neg. Lwn(λ) values with the MUMM AC
method STD, MUMM and NIR-SWIR tend
to underestimate Lwn(λ) (bias
between -26 and 3%) STD, MUMM and NIR-SWIR
perform better in the green (< 20% RE), not as well in the blue & red (> 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
Goyens et al., 2013
I. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
b. b. Water class-Water class-specific validationspecific 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
b. b. Water class-Water class-specific validationspecific 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. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
b. b. Water class-Water class-specific validationspecific 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. ρw(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 ρw(λ) vs.
satellite ρw(λ)
Global validation
Water class-specific validation
I. Evaluation and comparison of existing turbid water AC methods
Determine best Determine best AC method(s) AC method(s) to improve !to improve !
I. I. EvaluationEvaluation and comparisoncomparison of turbid water AC methods
c. c. ConclusionConclusion
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 Rrs(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 Rrs(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 Rrs(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 Rrs(NIR)• complicates BRDF (f/Q) corrections• sensor saturation
– shallow waters
Need for accurate data processing algorithms
ρt: total (measured)
ρr: Rayleigh (known)
ρa: aerosols (unknown)
ρra: rayleigh-aerosols (unknown)
ρwc:whitecaps (modelled/wind)
ρw: water (unknown, 10% of ρt)
ρg: glitter (masked or modelled
Objectives: 5% for absolute radiances and 2% for relative reflectances
Rationale of atmospheric correction
ρt: total (measured)
ρr: Rayleigh (known)
ρa: aerosols (unknown)
ρra: rayleigh-aerosols (unknown)
ρwc:whitecaps (modelled/wind)
ρw: water (unknown, 10% of ρt)
ρg: 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
670nm 765nm 865nm
Hypothesis: absorbing ocean w negligeable
t()-r()=ra(a(A()
Classic Black Pixel Assumption (1/2)NIR bands
670nm 765nm 865nm
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 ρA and t
NIR bands
670nm 765nm 865nm
Hypothesis: absorbing ocean w negligeable
t()-r()=ra(a(A()
Classic Black Pixel Assumption (2/2)
412nm 443nm 490nm 510nm 555nm
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. 2000• use shortwave infrared bands e.g., Wang & Shi 2007• use of UV bands He et al., 2012
• correct/model the non-negligible Rrs(NIR)
Lavender et al. 2005 MERIS Bailey et al. 2010 used in SeaWiFS Reprocessing 2010 Moore et al. 1999 MERIS/OLCI Wang et al. 2012 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 Rrs(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. 2000• use shortwave infrared bands e.g., Wang & Shi 2007• use of UV bands He et al., 2012
• correct/model the non-negligible Rrs(NIR)
Lavender et al. 2005 MERIS Bailey et al. 2010 used in SeaWiFS Reprocessing 2010 Moore & Lavender, 1999 MERIS/OLCI Wang et al. 2012 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 Rrs(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. 2000• use shortwave infrared bands e.g., Wang & Shi 2007• use of UV bands He et al., 2012
• correct/model the non-negligible Rrs(NIR)
Lavender et al. 2005 MERIS Bailey et al. 2010 used in SeaWiFS Reprocessing 2010 Moore & Lavender 1999, MERIS/OLCI Wang et al. 2012 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; Doerrfer & Schiller, 2008; Kuchinke et al. 2009; Schroeder et al., 2007; Steinmetz et al., 2010; Brajard et al., 2012
Approaches to account for Rrs(NIR) > 0 sr-1 overlap
Courtesy of Jeremy Werdell
Kratzer et al., 2010
He et al., 2012
USE OF UV BANDS