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NIR‐Red Models for Estimation of Chlorophyll‐a Concentration –
Application to Aircraft and Satellite Data
Wesley J. Moses1,*, Anatoly A. Gitelson1, Vladislav Saprygin2, Sergey Berdnikov2, Vasiliy Povazhnyi2, Daniela Gurlin1, and Alexander A. Gilerson3
1University of Nebraska‐Lincoln, NE, USA.
2Southern Scientific Center of the Russian Academy of Sciences, Rostov‐on‐Don, Russia
3The City College of the City University of New York, NY, USA.
*Currently at Naval Research Lab, Washington, D.C.
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NIR‐red Algorithms for Satellite Data
Required Conditions
• Algorithm should be
– Consistently highly accurate over a wide range of chl‐a concentrations
– Resistant to variations in biophysical characteristics of water
– Sufficiently resistant to atmospheric effects or can be coupled with a reliable atmospheric correction procedure
Application of NIR‐Red Model to Airborne Data
AISA‐Eagle (Airborne Imaging Spectrometer for Applications)
‐ maximum of 256 continuous spectral channels
‐ range 400 – 970 nm
‐ max. spectral resolution 2.3 nm
‐ peak signal‐to‐noise ratio 490:1
‐ spatial resolution 2 m at 10,000 ft above ground 3
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Data
• Five data collection campaigns on the Fremont State Lakes – 02 July, 14 July, 26 Sep, 25 Oct, and 19 Nov of 2008
Date
Min.
Median
Max.
Mean
Standard
Deviation
02 July 08
14 July 08
26 Sep 08
25 Oct 08
19 Nov 08
4.35
6.59
8.47
9.42
2.07
16.20
13.54
31.06
27.02
20.25
22.68
20.80
68.62
69.23
74.19
14.04
13.80
31.11
32.23
26.85
6.68
5.98
19.04
21.33
25.11
Coefficient of Number of
Variation
Stations
0.48
0.43
0.61
0.66
0.94
Descriptive statistics of chl-a data from the five campaigns
7
6
8
6
8
5
Two‐Band NIR‐Red Model for AISA Data
25
20
r = 0.93
15
10
02 Jul
5
y = 70.718x - 62.001
2
20
80
y = 131.63x - 117.5
60
r = 0.9
-3
2
Chl-a (mg m )
-3
Chl-a (mg m )
y = 72.764x - 63.855
r = 0.89
15
10
14 Jul
5
2
40
1.1
0
0.9
1.2
1
1.1
1.2
0.9
1.1
R 704 /R 676
R 704 /R 676
y = 108.15x - 88.981
60
r = 0.87
80
y = 156.06x - 132.17
-3
80
2
40
25 Oct
20
1.3
R 704 /R 676
Chl-a (mg m )
1
-3
0
2
60
r = 0.92
40
19 Nov
20
0
1.1
1.3
1.5
1.7
0.8
1
R 704 /R 676
1.2
1.4
R 704 /R 676
80
-3
0.9
Chl-a (mg m )
0.9
26 Sep
20
0
0
Chl-a (mg m )
-3
Chl-a (mg m )
25
02-Jul
60
14-Jul
40
26-Sep
20
25-Oct
19-Nov
0
0.9
1
1.1
1.2
1.3
R 704 /R 676
1.4
1.5
1.6
1.5
6
At‐Sensor Reflectance Spectra Before Atmospheric Correction
02 Jul (a)
0.06
0.04
-1
-1
Rrs (Sr )
0.05
Rrs (Sr )
(e)
19 Nov
0.05
0.04
0.03
0.03
0.02
0.02
0.01
400
0.01
400
500
600
700
800
900
Wavelength (nm)
600
700
800
900
Wavelength (nm)
Before atmospheric correction
After QUAC atmospheric correction
80
14-Jul
40
26-Sep
20
25-Oct
19-Nov
0
0.9
1
1.1
1.2
1.3
R 704 /R 676
1.4
1.5
1.6
-3
02-Jul
60
Chl-a (mg m )
80
-3
Chl-a (mg m )
500
60
02-Jul
R2 = 0.92
40
14-Jul
26-Sep
2008 NE Calibration
20
0
0.8
1.3
1.8
25-Oct
19-Nov
2.3
R 704/R 676
(Moses 2009; Moses et al. 2012a)
Chlorophyll‐a Maps from AISA‐Eagle Data 7
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Application to Airborne Data ‐ Conclusion
• MERIS NIR‐red model highly accurate, even without atmospheric correction
• Relationship affected by non‐uniform atmospheric effects
• Atmospheric correction by relative adjustment of input parameters resulted in conformity of the slope and offset for all the AISA images
• With robust atmospheric correction, the two‐band MERIS NIR‐red model can be calibrated to accurately estimate chl‐a
concentration
Application of NIR‐red Algorithms to MERIS Data
Data:
18 campaigns between March and October in 2008 – 2010 on the Taganrog Bay and Azov Sea
Taganrog Bay
Azov Sea
(maps from http://maps.google.com/)
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Application of NIR‐red Algorithms to MERIS Data
• 18 stations from 2008 and 113 stations from 2009‐2010
• 2008 stations – for calibration
• 2009‐2010 stations – for validation
Min
Max
Median
Mean
0.63
65.51
24.35
26.97
Descriptive statistics for chl‐a concentration (mg m‐3) in the calibration dataset
Min
Max
Median Mean
1.09
107.82 22.39
31.74
Descriptive statistics for chl‐a concentration (mg m‐3) in the validation dataset
Calibration of NIR‐red Algorithms with MERIS bands 11
2008 Azov Sea/Taganrog Bay
MERIS 2‐Band
80
2008 NE Line
-3
Azov Sea
60
Chl-a (mg m )
80
-3
Chl-a (mg m )
MERIS 3‐Band
2008 NE Line
40
2
r = 0.95
20
Azov Sea
60
40
2
r = 0.97
20
0
0
-0.1
0
0.1
0.2
0.3
0.6
1.1
(1/R 665 - 1/R 708 ) x R 753
1.6
2.1
R 708 /R 665
Moses et al. 2009

1
1

Three‐band MERIS NIR‐red Algorithm: Chl-a  232.29[ R665  R708  R753 ]  23.174
Two‐band MERIS NIR‐red Algorithm:
1
Chl-a  61.324[ R665
 R708 ]  37.94
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Validation of NIR‐red algorithms
2008 – 2010 Azov Sea
MERIS 2‐Band
120
1:1 Line
120
90
60
-3
RMSE = 6.68 mg m
30
-3
MAE = 5.12 mg m
0
0
30
60
90
120
-3
Estimated Chl-a (mg m )
In Situ Chl-a (mg m-3)
In Situ Chl-a (mg m-3)
MERIS 3‐Band
1:1 Line
90
60
-3
RMSE = 6.04 mg m
30
-3
MAE = 4.57 mg m
0
0
30
60
90
120
-3
Estimated Chl-a (mg m )
(Moses et al. 2012b)
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Validation of Advanced NIR‐red Algorithms
(Gilerson et al. 2010)
2008 – 2010 Azov Sea
Advanced MERIS 3‐Band




Advanced MERIS 2‐Band
1.124

120
1:1 Line
90
60
-3
RMSE = 5.91 mg m
30

-3
MAE = 4.71 mg m
0
30
60
90
-3
Estimated Chl-a (mg m )
120
1.124
1:1 Line
90
60
-3
RMSE = 5.92 mg m
30
-3
MAE = 4.32 mg m
0
0

1
Chl-a  35.75  R665
 R708  19.3
In Situ Chl-a (mg m-3)
In Situ Chl-a (mg m-3)
120


1
1
Chl-a  113.36  R665
 R708
 R753  16.45
0
30
60
90
-3
Estimated Chl-a (mg m )
(Moses et al. 2012b)
120
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Hyperspectral Imager for the Coastal Ocean (HICO)
(taken from Gitelson et al. 2011b)
15
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Challenges & Limitations
Temporal Variation of Water Quality
Satellite captures image in a few seconds – in situ data collection takes hours
Problem is magnified when same‐day cloud‐free image is not available
The effect of temporal variability is not uniform but specific to each water body 17
Challenges & Limitations
Within‐Pixel Spatial Heterogeneity
Point in situ data may not represent satellite pixel area (MERIS – 260 m x 290 m; MODIS– 1 km x 1km)
Plot of fluorescence readings along a transect on the Azov Sea/Taganrog Bay
Fluorometer Values
5
1 km
4.6
4.2
3.8
3.4
300 m
3
0
300 m
300
300 m
600
Distance (m)
900
1200
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Challenges & Limitations
Atmospheric Correction
No atmospheric correction procedure worked consistently well for MODIS Data
Bright Pixel Atmospheric Correction (Moore et al. 1999) gave good results for MERIS data
Nevertheless, it yielded negative reflectances for several stations
Conclusion
• The NIR‐red models are consistently highly accurate over a wide range of chl-a concentrations, including low‐to‐
moderate values
• The NIR‐red models need not be re‐parameterized for different water bodies
• The two‐band MERIS NIR‐red model is well‐suited for estimating chl-a concentration in turbid productive waters using satellite data
• With well‐adapted in situ data collection techniques, the NIR‐
red algorithms can be robustly calibrated
• Results point to the possibility of universal NIR‐red algorithms
that can be broadly applied to satellite data from inland, estuarine, and coastal waters around the globe
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Acknowledgements
• Field data collection in the Azov Sea and Taganrog Bay were conducted by teams at the Institute for Environmental Quality, Kiev, Ukraine, and the Southern Scientific Center of the Russian Academy of Sciences, Rostov‐on‐
Don, Russia
Thank You!
Contact: [email protected]
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