Upload
phungmien
View
220
Download
0
Embed Size (px)
Citation preview
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