Transcript Document
Verification and Application of a Bio-optical Algorithm for Lake Michigan using SeaWiFS: a Seven-year Inter-annual Analysis
Remote Sensing Across the Great Lakes: Observations, Monitoring and Action April 4-6, 2006, Rochester, NY
R. Shuchman C. Hatt Altarum D. Pozdnyakov A. Korosov NIERSC
www.altarum.org
G. Leshkevich NOAA GLERL
Outline
Water Quality Retrieval Algorithm Overview Algorithm Validation Example Results for Lake Michigan Climate Change Modeling
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Water Quality Retrieval Algorithm
Uses any visible spectrum sensing satellite Detects spatial and temporal patterns in inland water bodies, including extreme and episodic events Partnership between – Altarum Institute – Nansen International Environmental and Remote Sensing Centre (NIERSC) – NOAA GLERL – University of Michigan / Western Michigan University
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Water Quality Retrieval Algorithm
Retrievables: Color Producing Agents (CPAs) – concentrations of phytoplankton chlorophyll (CHL) – suspended minerals (SM) – dissolved organic matter (DOC) Specific features: Satellite- and water body-non-specific Based on a hydro-optical model: Specific backscattering and absorption coefficients of CHL, SM and DOC Combines Neural Networks with a Levenberg-Marquardt multivariate optimization procedure – the combination renders the algorithm computationally operational Possesses quality assurance – Removal of pixels with poor atmospheric correction (SeaWiFS/MODIS standard procedures are applicable) – Removal of pixels that cannot be characterized by the hydro-optical model
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Algorithm Flow Chart
Multispectral satellite image Assesment of the quality of atmospheric correction
R rsw1
Atmospheric correction Calculation of
nL w R
,
F rs w
, 0 0
f
(
nL w
,
F
0 , 0 )
R rsw 1 R rsw1
Module of neural networks NN - 1
chl
: 0 - 70 ug/l;
sm:
0 - 25 mg/l;
doc:
0 - 25 mgC/l; Hydrooptical model
a b b
a i
*
C b b i
*
C i i
C
chl
C
sm
Selection of appropriate NN
C
doc R rsw
f
(
a
,
b b
)
chl
< 5
sm
< 5
doc
< 5 Yes NN - 2
chl:
0 - 5; No Yes NN - 3
sm
0 - 5; No Yes NN - 4
doc
0 - 5;
C
chl
C
sm
Computation of ranges of starting
Со C
doc
No
Co
Levenberg - Marquardt multivariate optimization procedure
C
Assesment of adequacy of the applied hydrooptical model Reconstruction of
R rsw
f
(
C chl
,
C sm
,
C doc
)
R rsw R rsw2
with
R rsw1 C
chl
C
sm
Concentration vector for each pixel
C
doc
Establishing of flags
C
Definitive decision on the adequacy of retrieval results
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Remote Sensing Reflectance
Measured Modeled
L u S
( 0 ) :
L u E d
( 0 ) ( 0 ) R Upwelling spectral radiance at the water
C
f
( ,
C
{
C chl i a
{
a
1 1 , 2 , 3 ...
n
*
C
1 ,
a
,
b b
) ,
C sm
...
,
C doc a i
*
C i
} } surface
a i
: Specific absorption coefficient for the i th water constituent
E d
( 0 ) : Downwelling spectral irradiance at the water surface
j b b
{
b
1 , 2 , 3 ...
m b
1 *
C
1 ...
b
*
bj C j
}
b bj
: Specific backscattering coefficient for the j th water constituent
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The Levenberg-Marquardt Multivariate Optimization Procedure (1)
S j L u
( 0 , )
E d
( 0 , )
wavelength
j
(such as a measured from a satellite)
R rsw
f
( ,
a,
b
)
reconstructed remote sensing reflectance,
C
{
C chl
,
C sm
,
C doc
)}
The residual between the following ways:
S j
and
R rsw j
can be computed by one of
g j
(
S j
R rswj
) /
S j
The multidimensional least-square solution using all wavelengths is found by minimizing the squares of the residuals:
f
j g
2
j
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The Levenberg-Marquardt Multivariate Optimization Procedure (2)
The absolute minimum of f(C) can be found with the Levenberg-Marquardt finite difference algorithm.
An iteration procedure is initiated by creating an array of initial guess values C
0
. Each initial guess value is adjusted so that f(C) approaches a minimum. The value of C that provides the smallest f(C) can be determined to be the solution to the inverse problem.
The number N of the initial vectors should not be excessively high because the computation time for the inverse problem solution increases proportionally with N.
But the use of an array of initial vectors does not guarantee that the iterative procedure be converging, or/and the eventually established concentration vector be realistic. To help avoid this outcome and to speed up the algorithm, a priori limits are set based upon realistic concentration values.
C i
min
C i
C i
max
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Hydro-optical model
Used to reconstruct remote sensing reflectance from water parameters Consists of a matrix of absorption and backscattering coefficients at each band wavelength for Chl, DOC and SM.
Initial HO model was based on Lake Ontario measurements from the 1980’s Varies between different water bodies due to a difference in types of Chl, DOC and SM. Therefore, a hydro-optical model based upon one body of water may not be applicable to another.
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The Algorithm Validation
Two shipborne campaigns: June and September 2003 Historical data: 1998 – 2004 (GLERL, EEGLE)
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Validation Data Collection
Satlantic Optical In Water Profiler
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Sampling Sites in the Vicinity of Kalamazoo River
2 1.8
1.6
1.4
1.2
1 0.8
0.6
0.4
0.2
0 RS mean chl, ug/L In-situ mean chl, ug/L Jul-A Jul-B Jul-C Sep-B
Sampling time-place
Comparison of the
chl
concentrations (in g/l), obtained from
in situ
measurements (grey) and those retrieved from remote sensing data averaged over 9 neighboring pixels (black).
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Sampling Sites in the Vicinity of Kalamazoo River
30 25 20 15 10 5 RS mean doc, mgC/L In-situ mean doc, mgC/L 0 Jul-A Jul-B Jul-C Sep-B
Sampling time-place
Comparison of the
doc
concentrations (in mgC/L), obtained from
in situ
measurements (grey) and those retrieved from remote sensing data averaged over 9 neighboring pixels (black).
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Sampling Sites in the Vicinity of Kalamazoo River
12 10 8 6 4 RS mean sm, mg/L In-situ mean sm, mg/L 2 0 Jul-A Jul-B Jul-C Sep-B
Sampling time-place
Comparison of the
sm
concentrations (in mC/L), obtained from
in situ
measurements (grey) and those retrieved from remote sensing data averaged over 9 neighboring pixels (black).
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Lake Michigan Characteristic Features
Dimictic lake (two overturns: the lake is vertically well mixed only from December to May) Wind-driven circulation (coastal jets) Episodic events: springtime resuspension (strong northerlies) and autumnal Ca precipitation (high water temperature Wind Driven Circulation
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Seasonal Variations of Retrieved CPAs 24 March 1998
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Seasonal Variations of Retrieved CPAs 17 April 1998
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Seasonal Variations of Retrieved CPAs 12 July 1998
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Seasonal Variations of Retrieved CPAs 25 August 1998
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Seasonal Variations of Retrieved CPAs 28 November 1998
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Correlation Between Southern Lake Averaged sm and Northern Winds During Feb/March (r = 0.95)
QuickSAT data 1.2
1.1
2002 1 0.9
2003 2001 0.8
0.7
0.6
0.5
2004 2000 0.4
0 2 4 6 8 10 number of days in February and March with strong northern winds 12
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Correlation Between Southern Lake Averaged sm and Surface Temperature in August (r = 0.85)
AVHRR Pathfinder data 3 2.5
2 1.5
1 0.5
0 20.5
1999 2002 2001 2000 2003 21 21.5
22 22.5
average water surface temperature, C 23
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1998 23.5
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Monthly Variation of Area Averaged sm and doc during Spring Episodic Event for 1998 in Southern Lake Michigan
4 3.5
3 2.5
2 1.5
1 0.5
0 11/26/97 01/25/98 03/26/98 Date
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05/25/98 07/24/98 DOC SM 23
Spatial Distribution of (a) sm surface concentration, and (b) the sm Voluminal Content Per Square Kilometer
Value for March 24, 2004 Mean March value Within along shoreline strip (Metric tons) 570,000 570,000
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Within off-shore outgrowth (Metric tons) 300,000 420,000 24
A Comparison of (a) the Spatial Distribution the sm Voluminal Content Per Square Kilometer, and (b) the Contours (in meters) of Bottom Sediment Accumulations Reported by Schwab et al.
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A Comparison of Time Variations in doc and River Discharge for Grand River through 1998-2003
river discharge doc 12000 10000 8000 6000 4000 2000 0 1.5
1 0.5
0 3.5
3 2.5
2
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date
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Climate change scenario
Climate Change
Remote sensing in the visible as a companion tool for lake monitoring Lake reaction Changes observations from space
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Climate Change Scenarios for Lake Michigan: Major Ecological Consequences and Potential Identification from Space
Climate change scenario
Increase in air temperature
Initial lake reaction
Increase of depth averaged water temperature
Ensuing lake reaction Changes in observed CPAs Major ecologic consequence
Decrease of ice concentration, disappearance of shore-bound ice, increase of ice-free period, extension of water stratification period
A
. Earlier onset of spring resuspension events, increase in concentration, decrease of content
doc sm
B
. Earlier onset and increase of duration of autumnal calcium carbonate precipitation event, decrease of doc content
A
. Increase of nutrient availability in spring, intensification of vernal phytoplankton growth
,
alterations of heterotrophic bacterial activity, increase of water toxicity
B.
Increase of nutrient availability, intensification of phytoplankton growth, alterations of heterotrophic bacterial activity
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Climate Change Scenarios for Lake Michigan: Major Ecological Consequences and Potential Identification from Space
Climate change scenario
Decrease of atmospheric precipitation
Initial lake reaction
Decrease of river discharge
Ensuing lake reaction
Decrease of input of
sm
and allochtonic
doc
, decrease of water turbidity in coastal zone
Changes in observed CPAs
Decrease of
sm
and
doc
concentrations, increase of photic depth in coastal zone
Major ecologic consequence
Alterations of chl vertical profile, intensification of deep-layer chl, alterations of bacterial activity in coastal zone Increase of atmospheric precipitation Increase of river discharge Increase of input of
sm
and allochtonic
doc
, increase of water turbidity in coastal zone Increase of
sm
and
doc
concentrations, decrease of photic depth in coastal zone Alterations of chl vertical profile, depletion of deep-layer chl, alterations of bacterial activity in coastal zone
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Future Steps
The generation of specific hydro-optical models for each of the Great Lakes using radiometric data at the MODIS visible bands and coincident
in situ
measurements of color-producing agents.
Examining the temporal and spatial variations of the hydro optical properties of Lake Erie.
The generation of a better atmospheric correction model for coastal regions in order to have more “usable” pixels in these areas.
The adaptation of the algorithm for use with hyper-spectral imagery from the Hyperion sensor, in order to obtain images of color-producing agents that are more accurate and have better (30 m) spatial resolution.
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Further Information
Description of the Algorithm: Pozdnyakov, D., R. Shuchman, A. Korosov, and C. Hatt. 2005. Operational algorithm for the retrieval of water quality in the Great Lakes.
Remote Sensing of Environment
. 97: 353-370. Application to Lake Michigan: Shuchman, R., A. Korosov, C. Hatt, D. Pozdnyakov, J. Means, and G. Meadows. 2005. Verification and Application of a Bio-optical Algorithm for Lake Michigan using SeaWiFS: a Seven-year Interannual Analysis.
Journal of Great Lakes Research
. (in press, expected June 2006) Contact Email: [email protected]
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