A score metrics for quality assurance of spectral remote ... · of spectral remote sensing...

A score metrics for quality assurance A score metrics for quality assurance of spectral remote sensing reflectance of spectral remote sensing reflectance

in aquatic environmentsin aquatic environments

Jianwei Wei, Jianwei Wei, ZhongpingZhongping LeeLee____________________________________________________________________

U i it f M h tt B tU i it f M h tt B tUniversity of Massachusetts BostonUniversity of Massachusetts Boston

Validation and Evaluation of Validation and Evaluation of RrsRrs DataData

Linear fit0.02

1 1 Error statistics are derived Error statistics are derived to quantify the overall to quantify the overall quality of quality of RrsRrs data at any data at any specific band:specific band:

Linear fit:y = 1.11*x

0 01

0.015

Rrs

(sr-1

) 1:1

specific band:specific band:

Diff% = ?Diff% = ?RMSE = ?RMSE = ?

0.005

0.01

Sate

llite

(443 nm) RR22 = ?= ?……

00 0.005 0.01 0.015 0.02

In-situ Rrs (sr-1)

(443 nm)

However, this validation practice does not tell the quality for individual Rrs spectrumquality for individual Rrs spectrum.

Scatter plots do not tell the quality of Scatter plots do not tell the quality of individualindividual RrsRrs spectrumspectrumindividualindividual RrsRrs spectrumspectrum

0.015

sr)

0.015

r)

0.005

0.010

Rrs (

1/s

0.005

0.010

Rrs (

1/sr

0.000400 500 600 700

Wavelength (nm)

0.000400 500 600 700

Wavelength (nm)

0.010

ed"

Rrs 1:1 0.010

red"

Rrs 1:1

0.000

0.005

"Mea

sure

0.000

0.005

"Mea

sur

0.0000.000 0.005 0.010

True Rrs

0.000 0.005 0.010

True Rrs

Key Points and ObjectiveKey Points and ObjectiveKey Points and ObjectiveKey Points and Objective

Both the shape and magnitude of Both the shape and magnitude of RrsRrs spectrum matterspectrum matter Use of the scatter plot at one wavelength does not tellUse of the scatter plot at one wavelength does not tell

the quality for individualthe quality for individual RrsRrs spectrumspectrumthe quality for individual the quality for individual RrsRrs spectrum.spectrum. Our objective is to develop a system (score metrics) to Our objective is to develop a system (score metrics) to

QA/AC individual QA/AC individual RrsRrs spectrum.spectrum.

Spectral Spectral RrsRrs Data in Global OceansData in Global Oceans

83% 6%Above-water

depth profilesMap of Sampling Areas

11% floating (SBA)

Skylight blocking apparatus (SBA)

Database of MultiDatabase of Multi--band band RrsRrs SpectraSpectra

9 wavelengths (412, 443, 488, 510, 531, 547, 555, 667, 685 nm) are assembled from those of rs

(1/s

r)

are assembled from those of MODIS, SeaWiFS, MERIS, and VIIRS sensors.

Rr

412 443 488 510 531 547 555 667 685

SeaWiFS × × × × × ×

MODIS × × × × × ×

MERIS × × × × × ×MERIS × × × × × ×

VIIRS × × × × ×

Classification of Optical Water Type (OWT)Classification of Optical Water Type (OWT)

Normalization: 1/22

1

( )( )( )

λλλ

=

=

rsrs n

rs ii

RnRR

Cosine distance Number of clusters is determined by GAP statistic

1= i

ForelForel--UleUle color scales (total 21)color scales (total 21)23 Water Types23 Water Types

0.7

0.8

0 3

0.4

0.5

0.6

0.7

ed R

rs

0

0.1

0.2

0.3

400 500 600 700

Nor

mal

ize

400 500 600 700

Wavelength (nm)

23 Optical Water Types23 Optical Water Types

Score Metrics

1. Water classification using Spectral Angle Mapper (SAM)2. Comparison with the reference spectra, band by band:

Credit 1, if nRrs(λi) within boundaries; Credit 0, otherwise

1 2( ) ( ) ... ( ) cosλ λ λ α+ + += ×ntot

C C CSN

3. Mean of the total credits as the final score:

0.4

0.5

0.2

0.3

nRrs

Boundary

0

0.1Reference Rrs

Boundary

400 500 600 700Wavelength (nm)

U.S. Northeast Coasts and Atlantic OceanU.S. Northeast Coasts and Atlantic OceanOptical Water TypesOptical Water Types Scores ofScores of RrsRrs SpectraSpectra

A2015258

Optical Water TypesOptical Water Types

A2015258

Scores of Scores of RrsRrs SpectraSpectra

0 003 Gulf of Maine

0

0.001

0.002

0.003Rr

s (1/

sr)

In situMODISA

0.002

0.001

0

-0.001

0

400 500 600 700Wavelength (nm)

MODGLINT flags

0

-0.001

U.S. Northeast Coasts and Atlantic OceanU.S. Northeast Coasts and Atlantic OceanOptical Water Types Scores of Rrs Spectra

A2015345

Optical Water TypesA2015345

Scores of Rrs Spectra

0 008Station 20

Flagged as “PROD FAIL”

0.004

0.006

0.008

Rrs

(1/s

r)

MODISA

In situ

0

0.002

400 500 600 700

R

Wavelength (nm)

Stray light?

The Great LakesThe Great LakesOptical Water Types L k Mi hi S 8Optical Water Types

0.0025

0.0035

0.0045

1/sr

)

Lake Michigan, Sta. 8MODISAIn-situ

-0.0005

0.0005

0.0015

400 500 600 700

Rrs

(

A2012178

Scores of Rrs SpectraFlagged by MODGLINT & COASTZ

400 500 600 700Wavelength (nm)

Lake Erie

A2012178

Flagged by COASTZ

MODISA Monthly LevelMODISA Monthly Level--3 Ocean Color3 Ocean Color0.6

Optical Water Types

100%

0.4

0.5dailymonthly

0.6

0.8AquaReferenceBoundary

20021822002212 L3m 9km

Freq

uenc

y (

0 1

0.2

0.3

Rrs

0.2

0.4

Boundary

20021822002212_L3m_9km

Scores of Rrs Spectra Scores0 0.2 0.4 0.6 0.8 1

0

0.1

Wavelength (nm)400 500 600 7000

p

0.6

0.8AquaReferenceBoundary

Rrs

0.2

0.4

Boundary

Wavelength (nm)400 500 600 7000

Evaluation of InEvaluation of In--situ situ RrsRrs Data QualityData Quality

NOMAD 2010 v2.b

Numbers of data points: 4466 Period of data sampling: 1991-2007

0 9

Optical Water Types and Scores

f Rrs

spe

ctra

0 5

0.6

0.7

0.8

0.9

30

40

50

60

N = 1175

Sco

res

of

0 1

0.2

0.3

0.4

0.5

0

10

20

30 N 1175

Optical water types1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0.1 0

Effects of Effects of RrsRrs Data Quality on Model Data Quality on Model C ffi i tC ffi i tCoefficientsCoefficients

1023 )

101

CH

L (m

g m

-3

100

1 0 110-2

10-1 All data (N=1175)OC3mData (Score>0.9)New fit

MBR10-1 100 101

Only Rrs data of scores > 0.9 are used (N = 430) Th 4th d l i l i d The 4th order polynomial is assumed

ConclusionsConclusions

1. To QA/QC the individual Rrs spectrum, we have de eloped the score metrics based on in sit Rrs datadeveloped the score metrics, based on in situ Rrs data from a large range of waters.

2 The score metrics has been tested with MODIS Aqua2. The score metrics has been tested with MODIS Aqua ocean color data and in situ data, which has shown great performance.

3. This score metrics (Version 1.0) will be further improved as more and more good data are incorporated.

AcknowledgementsUniv. of South Florida: Chuanmin HuJoint Research Center: Giuseppe ZibordiXiamen University: Shaoling ShangDalhousie University: Marlon Lewis2012 Lake Michigan cruise: Christopher Strait, Mike Twardowski,

Colleen Mouw Nima PahlevanColleen Mouw, Nima PahlevanSeaBASS: data contributorsOBPG

Special thanks to the funding agencies:

NASA: Ocean Biology and Biogeochemistry, Energy and Water Cycle, and Applied Sciences programs

/NOAA: JPSS VIIRS Ocean Color Cal/Val Project