menghua wang, noaa/nesdis/star remote sensing of water properties using the swir- based atmospheric...
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Menghua Wang, NOAA/NESDIS/STAR
Remote Sensing of Water Properties Using the SWIR-based Atmospheric Correction Algorithm
Menghua Wang Wei Shi and SeungHyun Son
NOAA/NESDIS/STARE/RA3, Room 102, 5200 Auth Road
Camp Springs, MD 20746, [email protected]
MODIS Science Team MeetingMarriott Inn/Conference Center, College Park, Maryland, May 18-20, 2011
AcknowledgementsSupports from NOAA/NASA funding, NASA SeaBASS in situ data, in situ data from Lake Taihu (J. Tang and Y. Zhang), and MODIS Level 1B data.
Menghua Wang, NOAA/NESDIS/STAR
Atmospheric Correction: SWIR Bands(Wang & Shi, 2005; Wang, 2007)
At the shortwave infrared (SWIR) wavelengths (>~1000 nm), water has much stronger absorptions. Thus, atmospheric correction (Gordon & Wang, 1994) can be carried out for coastal regions with the black pixel assumption at the SWIR bands.
Water absorption for 869 nm, 1240 nm, 1640 nm, and 2130 nm are 5 m-1, 88 m-1, 498 m-1, and 2200 m-1, respectively.
MODIS has three SWIR bands at 1240, 1640, and 2130 nm, designed for atmosphere and land applications.
We use the SWIR band for the cloud masking. This is necessary for coastal region and inland lake waters.
Require sufficient SNR characteristics for the SWIR bands and the SWIR atmospheric correction has slight larger noises at the short visible bands (compared with those from the NIR algorithm).
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Menghua Wang, NOAA/NESDIS/STAR
nLw(443)Scale: 0.-3.0
(mW/cm2 m sr)
Wang, M., S. Son, and W. Shi (2009), “Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data,” Remote Sens. Environ., 113, 635-644.
July, 2005NIR-SWIR Data
Processing
July, 2005NIR-SWIR Data
Processing
Chlorophyll-a0.01-10 (mg/m3)
(Log scale)
Menghua Wang, NOAA/NESDIS/STAR
Using the SWIR algorithm, we have derived the water optical properties over the Lake Taihu using the MODIS-Aqua measurements during the spring of 2007 for monitoring a massive blue-green algae bloom, which was a major natural disaster affecting several millions residents in nearby Wuxi city.
Wang, M. and W. Shi (2008), “Satellite observed algae blooms in China’s Lake Taihu”, Eos, Transaction, American Geophysical Union, 89, p201-202, May 27.
Wang, M., W. Shi, and J. Tang (2011), “Water property monitoring and assessment for China’s inland Lake Taihu from MODIS-Aqua measurements”, Remote Sens. Environ., 115, 841-854.
The work was featured in the NASA 2008 Sensing Our Planet (http://nasadaacs.eos.nasa.gov/articles/2008/2008_algae.html)
Results from Inland Lake Taihu
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Menghua Wang, NOAA/NESDIS/STAR
The third largest fresh inland lake in China (~2,250 km2). Located in one of the world’s most urbanized and heavily populated areas. Provide water resource for several million residents in nearby Wuxi city.
Geo-location of Lake Taihu Geo-location of Lake Taihu Extremely
Turbid Waters
Menghua Wang, NOAA/NESDIS/STAR
The SWIR atmospheric correction algorithm (Wang, 2007; Wang & Shi, 2005) is used for the water property data processing.
Since MODIS 1240 nm band is not always black for the entire Lake Taihu, we have developed three-step method in the data processing for each MODIS-Aqua data file: First, regions for the black of 1240 nm band are determined using the
SWIR data processing. Second, a dominant aerosol model from the region with black of
1240 nm band is obtained, and Finally, with the derived aerosol model, the SWIR atmospheric
correction algorithm is run using only 2130 nm band (with fixed aerosol model).
The Lake Taihu water property data are then derived.
Methodology (1) Methodology (1)
Wang, M., W. Shi, J. Tang (2011), “Water property monitoring and assessment for China’s inland Lake Taihu from MODIS-Aqua measurement,” Remote Sens. Environ., 115, 841-854.
Menghua Wang, NOAA/NESDIS/STAR
Validation Results for MODIS-derived Water-leaving Radiance Spectra
Validation Results for MODIS-derived Water-leaving Radiance Spectra
10-1
100
101
400 500 600 700 800 900
In Situ DataMODIS-Aqua
( )Wavelength nm
, 10, 2007Lake Taihu in China June (a)
Location: (120.20oE, 31.43oN)
10-1
100
101
400 500 600 700 800 900
In Situ DataMODIS-Aqua
( )Wavelength nm
, 12, 2007Lake Taihu in China June
: (120.29Location o , 31.27E o )N
(b)
10-1
100
101
400 500 600 700 800 900
In Situ DataMODIS-Aqua
( )Wavelength nm
, 17, 2007Lake Taihu in China June
: (120.31Location o , 30.95E o )N
(c)
10-1
100
101
400 500 600 700 800 900
In Situ DataMODIS-Aqua
( )Wavelength nm
, 18, 2007Lake Taihu in China June
: (120.30Location o , 30.94E o )N
(d)
Menghua Wang, NOAA/NESDIS/STAR
Black pixel assumption at the SWIR 1640 and 2130 nm is generally valid for Lake Taihu.
The SWIR atmospheric correction algorithm using bands 1640 and 2130 nm (Wang, 2007) can be used for the water property data processing.
However, for MODIS-Aqua, four out of ten detectors for the SWIR 1640 nm band are inoperable (dysfunctional).
We focus on deriving seasonal results for the lake using the SWIR 1640 and 2130 nm atmospheric correction algorithm.
More in situ data (five seasonal cruises in 2006-2007 in the lake) are also available to us now.
Methodology (2) Methodology (2)
Menghua Wang, NOAA/NESDIS/STAR
Validation Results for MODIS-derived Water-leaving Radiance Spectra
Validation Results for MODIS-derived Water-leaving Radiance Spectra
Menghua Wang, NOAA/NESDIS/STAR
Validation Results in Lake Taihu (1)Validation Results in Lake Taihu (1)
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0 2 4 6 8 10
412 nm443 nm488 nm555 nm645 nm859 nm1240 nm
In Situ nLw(λ) ( mW cm-2 m
-1 sr-1)
Lake Taihu in China1640 & 2130 nm Method
λ ( ) nm Ratio Std 412 1.068 0.132 443 1.033 0.119 488 1.038 0.161 555 0.946 0.128 645 0.942 0.180 859 1.381 0.4561240 0.913 2.903
5 5 >x with 12 valid pixels
(a)
Menghua Wang, NOAA/NESDIS/STAR
Validation Results in Lake Taihu (2)Validation Results in Lake Taihu (2)
0
2
4
6
8
10
0 2 4 6 8 10
412 nm443 nm488 nm555 nm645 nm859 nm
In Situ nLw(λ) ( mW cm-2 m
-1 sr-1)
Lake Taihu in China1240 & 2130 nm Method( ., 2011)Wang et al
λ ( ) nm Ratio Std 412 0.832 0.125 443 0.885 0.111 488 0.968 0.116 555 0.937 0.090 645 0.930 0.141 859 1.495 0.532
5 5 >x with 12 valid pixels
(b)
Menghua Wang, NOAA/NESDIS/STAR
Validation Results in Lake Taihu (3)Validation Results in Lake Taihu (3)
0
2
4
6
8
10
0 2 4 6 8 10
412 nm443 nm488 nm555 nm645 nm859 nm
In Situ nLw(λ) ( mW cm-2 m
-1 sr-1)
Lake Taihu in China1640 & 2130 nm Method
5 5 x with≥5 valid pixels
(c)
Extreme in situnLw(859)
Menghua Wang, NOAA/NESDIS/STAR
Seasonal Histograms in nLw(1240)Seasonal Histograms in nLw(1240)
0.1
1
10
-0.1 -0.05 0 0.05 0.1 0.15 0.2
nLw(1240) (mW cm-2
m-1 sr
-1)
Lake Taihu in China- (2002-2010)MODIS Aqua
Winter
Summer
Spring
Fall
(a)
TOA Radiance at 1240 nm ~0.1 mW cm-2 um-1 sr-1
Menghua Wang, NOAA/NESDIS/STAR
Climatology Histogram in nLw(1240)Climatology Histogram in nLw(1240)
0
5
10
15
0
5
10
15
20
25
-0.1 -0.05 0 0.05 0.1 0.15 0.2
MODIS-Aqua
In Situ Data
nLw(1240) (mW cm-2
m-1 sr
-1)
Lake Taihu in China- (2002-2010)MODIS Aqua
All Data
# : 135Total of In Situ Data
(b)
TOA Radiance at 1240 nm ~0.1 mW cm-2 um-1 sr-1
Menghua Wang, NOAA/NESDIS/STAR
Conclusions
For the turbid waters in coastal regions and inland lakes, it has been demonstrated that the SWIR bands can be used for atmospheric correction.
Future ocean (water) color satellite sensor needs to include the SWIR bands with sufficient SNR values for cases with turbid waters.
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Menghua Wang, NOAA/NESDIS/STAR
Comparisons Between MODIS and In Situ (SeaBASS) Data
NIR-SWIR Method Product Data #
Slope Int† R‡ Mean Ratio€ Medium Ratio&
nLw(412) 116 0.827 0.198 0.873 1.120 1.084
nLw(443) 128 0.952 0.073 0.891 1.040 1.025
nLw(488) 104 1.057 -0.050 0.951 0.997 1.015
nLw(531) 32 1.066 -0.069 0.961 1.018 1.040
nLw(551) 116 1.106 -0.046 0.968 1.047 1.055
nLw(667) 97 0.822 0.014 0.727 1.296 1.129
Overall nLw( )
593 0.998 0.024 0.935 1.090 1.030
nLw(443)/ nLw(551)
43 1.021 0.025 0.879 1.052 1.032
nLw(488)/ nLw(551)
43 1.034 -0.023 0.953 1.017 0.996
†Intercept for lin efi t ‡Correlatio ncoefficient €Mean ratio of MODIS v .s in situ dat a &Medium ratio of MODIS v .s in situ data