Accessing SeaDAS Atmosphere Correction · Introduction • SeaDAS (SeaWiFS Data Analysing System)...

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Accessing SeaDAS Atmosphere Correctionfor Ocean Remote Sensing

Yi Qin1 and Jim Davies2

1CSIRO Land and Water, Canberra2CSIRO Marine and Atmosphere Research, Floreat

Introduction

• SeaDAS (SeaWiFS Data Analysing System) is a comprehensive package developed by NASA to provide ocean colour processing including atmosphere correction for multiple sensors (MODIS, SeaWiFS and others)

• Atmosphere correction is critical in ocean colour remote sensing, because the signal at sensor level is dominated (~90%) by contribution from atmosphere

• Aerosol is the least known and mostly changing factor in atcor. As a global standard package, yet SeaDAS only includes a limited set of 12 aerosol models to represent the vastly varying aerosols

• Is this set of aerosol models adequate for us (in the Australia region)?

• What is the overall performance of SeaDAS on atmosphere correction?

SeaDAS Atmosphere Correction

• Corrections to be performed:Rayleigh scattering

Aerosol scattering and absorption

Absorption by water vapor, oxygen and ozone

Glint correction

Whitecap correction

Sensor sensitivity to polarization state

• Dynamic aerosol determinationMultiple options provided in SeaDAS. The commonly used (and default) algorithm will be tested. This algorithm determines for each pixel, using information presented in the image itself, two aerosol models bounding the properties of aerosol over the pixel and an interpolation ratio.

• Detail of SeaDAS atcor in MODIS ATBD – MOD17

Approach

5: Assessment

4

2

RT Simulation& Image

Synthesize

L1BImage(TOARadiance)

WaterLeaving

Radiance(Simulated)

Met. Files

1

AerosolModels

WaterParam.

OtherParameters

3SeaDAS

Water-leavingRadiance

(Retrieved)

AerosolProperties(Retrieved)

Accuracy on water leaving radiance retrieval?

Aerosol model selection and aerosol optical depth retrieval?

Error on chl. retrieval due to atcor error?

mini-SeaDAS

Chl. (from twosets Lw)

Aerosol Models

• Rayleigh atmosphere (no aerosol) – Rayleigh Scat.

• SeaDAS internal models (12 in total) – Dyn. Aer. Det.

• Australian local aerosol data: Rottnest Island CIMEL measurements, 2001 to 2002, quality controlled, and inverted by AERONET standard algorithm. All data are grouped and averaged into 16 samples, according to:

origin (classified using reverse airmass trajectory by Hysplit as either maritime or continental),

asymmetry factor,

single scattering albedo and

angstrom coefficient

Aerosol Models: Internal and Local

0

0.02

0.04

0.06

0.08

0.1

0.12

400 600 800Wavelength (nm)

Aer

osol

Opt

ical

Dep

th

0.75

0.8

0.85

0.9

0.95

1

400 600 800Wavelength (nm)

Sing

le S

catt

erin

g A

lbed

o

Aerosol Optical Depth Single Scattering Albedo

Optical depth and single scattering albedo of SeaDAS internal aerosol models (lines only) compared to Rottnest Island aerosol data (lines with symbols).

Water Model ParameterizationsTable 1 properties of the average water

Chl 0.2115 CDOM 0.0404 Conc. TR 0.9000 bb_phy_slope 0.8000 bb_phy_542nm 0.0030 a_cdom_slope 0.0144 a_cdom_440nm 1.0000 a_tr_slope 0.0115 a_tr_440nm 0.0266 bb_tr_slope 0.8000 bb_tr_542nm 0.0074 bb2b_phy 0.0190 bb2b_tr 0.0247 Sp

ecifi

c In

herit

ed O

ptic

al

Prop

ertie

s (SI

OP)

a_phy (See Fig.)

0

0.01

0.02

0.03

380 580 780

• Three water parameterizations used representing average, clear and turbid waters respectively;

• The average water is taken from in-situ measurements at Mossman – Daintree region off Queensland

• Clean (clear) and turbid waters are derived by reducing / increasing the concentrations

Radiative Transfer Model

• COART (Coupled Ocean-Atmosphere Radiative Transfer) model was used in simulation:

based on DISORT, a well tested benchmark RT code;

handles rigorously the air-sea coupling;

shown to be able to model in-situ measurements with good agreement;

it is publicly available

• However, some problems with COART encountered during our simulations, due to recent code changes.

• Because of the importance of the RT code in this study, we have conducted some comparisons of COART with independent code:

COART vs. VGDOM for the case of Rayleigh atmosphere

COART vs. VGDOM_MC for general atmosphere-ocean system

COART vs. VGDOM: Rayleigh Atmosphere

• VGDOM is a radiative transfer code based on the rigorous algorithm of Discrete-Ordinate Method, and is developed by Yi Qin with Michael Box at UNSW

• Rayleigh backscattering contributes a dominant part of the totalatmosphere radiance

• In this comparison, VGDOM adopted the Rayleigh Optical Depth (ROD) model of SeaDAS

• Our comparison shows that COART and SeaDAS have consistent ROD model, and are essentially the same at 412nm, 443nm and 488nm bands

VGDOM_MC

• VGDOM can compute the Green’s function, but only deals with land surface

• Green’s function defines a photon’s probability of travelling in and out of any two (or the same) sides

• VGDOM_MC extends VGDOM to work with coupled atmosphere – ocean system, using a hybrid method of Green’s function and Monte Carlo simulation:

The Green’s function for atmosphere and ocean are computed by VGDOM

The Green’s function for the air-sea interface is computed using Monte Carlo

The whole system is combined using Monte Carlo again

Atmosphere

Air-Sea Interface

Ocean

COART vs. VGDOM_MC (1)Sensor level radiance Water leaving radiance

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View Zenith Angle

Radi

ance

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m-s

r)

COART

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View Zenith Angle

Radi

ance

(w/m

^2-u

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r)

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vgdom

Total above surface radiance Total sub-surface radiance

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View Zenith Angle

Radi

ance

(w/m

^2-u

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COART

vgdom

020406080

100120140160

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View Zenith AngleRa

dian

ce (w

/m^2

-um

-sr)

COART

vgdom

(WL = 443nm, sun zenith angle = 43 deg, relative azimuth angle = 145 deg)

COART vs. VGDOM_MC (2)Sensor level radiance Difference Water-leaving radiance Difference

View Zenith Angle (deg)

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ativ

e A

zim

uth

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eg)

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WL = 443nm, sun zenith angle = 43º. The range of sensor level radiance for the shown geometry is around 50 (at small zenith angles) to 100 (at large zenith angles), and water leaving radiance is around 6 to 8.

Radiative Transfer Simulation (1)

• Reflectance vs. radiance

• Angular resolution

• Spectral resolution – full band simulation (incl. out of band response) is not practical, therefore a linear model is used to relate band-weighted reflectance (based on full spectrum COART simulations) to a limited set of pseudo-monochromatic COART predictor channel reflectance (i.e. a subset of the full-spectrum simulation),

where the predictor channel and coefficients are determined by a“training set”, with a target of 1 NEdL (the equivalent noise level).

∑=p

ppb RCR


563 0.4053660 0.3830 670 0.4519 670 676 0.1676 739 0.1107 757 0.1336 758 0.1512 759 0.1046 763 0.1418

765

783 0.3571 525 0.0081 851 0.3520 862 0.1166 867 0.0296 874 0.2098 876 0.2057 885 0.0563

865

983 0.0187

SeaWiFS Band

Pred. Channel Coefficient

404 0.2957 414 0.3468 412 422 0.3576 435 0.3124 446 0.2418 447 0.2595 443

452 0.1866 482 0.3043 292 0.4044 490 500 0.2903 500 0.3900 512 0.2689 510 520 0.3391 545 0.3380 552 0.2541 555 563 0.4053 660 0 3830


Applying the obtained linear model to an independent set shows that only in 2 (out of 979) cases at 865 band the error exceeds 1 NEdL, in which they are less than 1.5 NEdL

L1B Image Synthesizing (1)

Synthetic images are constructed by taking real images and replacing the sensor radiances with simulated values, using geometry info extracted from accompanying geo-location file

Three MODIS and two SeaWiFS overpasses, representing typical summer and winter geometries in the east and west coast, are constructed

Example synthetic image

Original Synthetic

L1B Image Synthesizing (2)

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Pixel

L1B

Sca

led

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ger

Original Syn.

Example horizontal profile of synthesized image compared to the original image

Results – Water Leaving Radiance, Lw (1)


Lw error, dLw, is correlated to Rayleigh radiance, Lr. Shown below a case of one pixel and 9 MODIS bands

y = -0.0603x + 0.334R2 = 0.9112

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Lr - Rayleigh Radiance

dLw

dLwLinear (dLw)


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dLw - Lr correlation coefficient

Freq

uenc

y (%

)

AverageCleanTurbid

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dLw - Lr correlation coefficient

Freq

uenc

y (%

)

AverageCleanTurbid

For band 1 (412 nm) to 9 (869 nm) For band 2 (443 nm) to 9 (869 nm)

Frequency of correlation coefficient between dLw and Lr, for all valid pixels of all synthetic images

Results – Aerosol Model: AOD Retrieval (1)

0%5%

10%15%20%25%30%35%40%45%50%

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AOD Error

Freq

uenc

y

412443488

Frequency of AOD error, grouped by bands. Input AOD is 0.1.

Results – Aerosol Model : AOD Retrieval (2)

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AOD Error

Freq

uenc

y

A2004039A2004183A2005152

Frequency of AOD error, grouped by overpass. Input AOD is 0.1.


0%10%20%30%40%50%60%70%

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AOD Error

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uenc

y

Average

Clean

Turbid

Frequency of AOD error, grouped by water type. Input AOD is 0.1.


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AOD Error

Freq

uenc

y

Rayleigh

modisa_c50

RI_Smp_4

Frequency of AOD error, grouped by aerosol. Input AOD is 0.1.


Input

Frequency of retrieved aerosol optical depth over all geometries, water types and aerosol models. If retrieved correctly, the aerosol optical depth should be 0.1, the value input to COART.

Results – Aerosol Model: Model Selection (1)

Weighted frequency (%) of the aerosol models selected by SeaDAS for each of the 12 input models (listed in the first column), for the case of MODIS synthetic images. The diagonal cell is the frequency the model is selected correctly. The largest frequency in each row is shown in red, and the second in blue.

Input Model o99 m50 m70 m90 m99 c50 c70 c90 c99 t50 t90 t99 o99 60.8 0.7 0.6 1.2 10.5 1.1 0.7 0.5 1.6 20.2 0.9 1.2 m50 14.6 22.2 17.3 12.2 8.0 0.0 6.1 12.3 7.2 0.0 0.0 0.0 m70 19.9 10.6 24.2 10.2 5.9 0.0 2.7 21.1 5.4 0.0 0.0 0.0 m90 34.1 0.0 0.6 24.6 11.9 0.0 0.0 7.0 21.8 0.0 0.0 0.0 m99 66.3 0.0 0.1 3.4 22.2 0.0 0.0 0.6 7.4 0.0 0.0 0.0 c50 6.4 15.9 7.5 4.7 7.7 18.3 20.4 6.5 4.9 0.0 0.8 6.8 c70 12.6 20.8 15.7 4.2 7.9 6.6 19.7 4.4 5.1 0.0 0.1 2.9 c90 30.0 3.8 6.8 23.9 2.9 0.0 0.6 21.4 10.5 0.0 0.0 0.0 c99 46.7 0.0 0.6 9.0 19.2 0.0 0.0 5.3 19.1 0.0 0.0 0.0 t50 0.2 1.9 2.1 1.0 0.0 7.8 2.9 1.4 0.7 34.1 28.0 19.8 t90 11.1 5.4 5.3 2.6 3.4 14.1 9.3 3.7 1.8 12.3 11.4 19.6 t99 23.7 6.3 4.4 1.7 2.4 20.1 13.6 2.6 1.4 0.2 6.5 17.1

Results – Aerosol Model : Model Selection (2)

Frequency (%) of the aerosol models selection for the case of SeaWiFS

second in italic bold.

Input Model o99 m50 m70 m90 m99 c50 c70 c90 c99 t50 t90 t99o99 31.9 9.4 8.9 17.0 3.5 1.8 5.7 13.3 6.2 0.3 0.3 1.6m50 2.7 3.9 3.4 1.2 0.2 40.2 12.5 0.1 0.4 0.7 9.4 25.3m90 0.0 21.9 18.9 0.0 0.0 18.6 21.3 9.2 0.0 0.4 0.4 9.4c50 0.0 0.0 0.0 0.0 0.0 25.6 2.2 0.0 0.0 0.7 39.3 32.2c90 0.0 13.4 2.4 0.0 0.0 37.3 16.5 0.0 0.0 0.5 10.0 19.9t50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0t99 0.0 0.0 0.0 0.0 0.0 2.0 0.5 0.0 0.0 85.6 10.9 1.2o99 30.7 12.3 9.4 6.2 1.2 5.8 9.4 17.4 3.5 0.6 1.6 1.8m50 2.3 2.7 1.0 0.4 0.4 10.7 4.5 0.3 0.2 10.2 25.3 41.9m90 0.0 14.1 8.8 0.0 0.0 21.8 25.7 0.0 0.0 0.8 9.4 19.5c50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40.0 32.2 27.8c90 0.0 2.1 0.0 0.0 0.0 14.5 13.5 0.0 0.0 10.5 19.9 39.5t50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0t99 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 96.4 1.2 2.0

Results – Influence of Lw Error on Chlorophyll Retrieval

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-30%

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-10% 0% 10%

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Relative Chl. Error

Freq

uenc

yAverage

Turbid

Frequency of chlorophyll error (%) due to Lw error, where positive is over estimated, for all the valid pixels. The SeaDAS OC3 algorithm is used to compute chlorophyll from water leaving radiance

Conclusions (1)

• Water leaving radiance, Lw, is retrieved consistently lower in the blue end and is consistent across all aerosol models (internal or local models), including Rayleigh atmosphere.

• Lw error is highly correlated with Rayleigh radiance, Lr. The Lw error is likely due to error (or difference) in Lr computing by SeaDAS and COART.

Note that the Rayleigh optical depth model should not be the reason of Lw error as illustrated by the comparison between COART and VGDOM outputs.

• The SeaDAS Lr table is generated with an analytical air-sea interface model, which could be a source of error and needs further investigation.

• For the standard chlorophyll retrieval by SEADAS (OC3-algorithm), the influence of Lw error is relatively small compared to the likely error of in-water inversions.

• For the average and turbid waters, the chlorophyll concentration error due to Lwerror is within 30% (note that the OC3 algorithm uses a spectral band ratio which may partly compensate the Lw error).

• Other inversion algorithms may be more sensitive to Lw errors.

Conclusions (2)

• Some local aerosol models (e.g.from Rottnest Island) fall outside of the range of the SeaDAS internal aerosol models,; however, there is no evidence to suggest that SeaDAS performance is poorer for the Rottnest Island models.

• SeaDAS is able to retrieve aerosol optical depth to within 50% of error, though it often picks up the wrong bounding aerosol models.

• Conclusions relate to ideal conditions, e.g., the amount of precipitable water and ozone, and the surface wind speed are provided in synthetic meteorological and ozone files (which might not always be realistic). No noise is added into simulated radiance. Issues of whitecap, sensor sensitivity to polarization are not considered in this report.

• Overall, we believe that in the Australian region (but dependant on water type!) MODIS and SeaWiFS atmosphere correction with SeaDAS is a less urgent issue than the in-water inversions, though several aspects can be improved.These aspects are:

The Rayleigh radiance (analytical water reflectance may not be suitable),

The aerosol model (needs Australia-specific models)

Acknowledgement

We would like to thank the following people for their support and help:

• Zhonghai Jin, Langley Research Centre, NASA

• Ross Mitchell, CSIRO Marine and Atmosphere Research

• Vittorio Bando, Arnold Dekker, David Blondeau-Patissier and other colleagues at Environmental RT Group, CSIRO Land and Water

• Peter Fearns and other colleagues at CSIRO Marine and AtmosphereResearch

Thank You!

Accessing SeaDAS Atmosphere Correction · Introduction • SeaDAS (SeaWiFS Data Analysing System)...

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