www.altarum.org

Verification and Application of aVerification and Application of aBio-optical Algorithm for Lake Michigan Bio-optical Algorithm for Lake Michigan using SeaWiFS:using SeaWiFS:a Seven-year Inter-annual Analysisa Seven-year Inter-annual Analysis

Remote Sensing Across the Great Lakes: Remote Sensing Across the Great Lakes: Observations, Monitoring and ActionObservations, Monitoring and Action

April 4-6, 2006, Rochester, NYApril 4-6, 2006, Rochester, NY

R. Shuchman D. Pozdnyakov G. LeshkevichC. Hatt A. Korosov

Altarum NIERSC NOAA GLERL

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OutlineOutline

Water Quality Retrieval Algorithm Overview

Algorithm Validation

Example Results for Lake Michigan

Climate Change Modeling

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Water Quality Retrieval AlgorithmWater 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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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

Water Quality Retrieval AlgorithmWater Quality Retrieval Algorithm

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Algorithm Flow ChartAlgorithm Flow Chart

Calculationof

Module of neural networksNN - 1

chl : 0 - 70 ug/l; sm: 0 - 25 mg/l; doc: 0 - 25 mgC/l;

NN - 2chl: 0 - 5;

NN - 3sm 0 - 5;

NN - 4doc 0 - 5;

Computation of ranges of starting Со

Levenberg - Marquardt multivariate optimization procedure

Assesment of adequacy of the applied hydrooptical

modelReconstruction of

Comparison ofR rsw2 with R rsw1

Hydrooptical model

C chl C sm C doc

Co

C chl

C

C doc

C

Atmospheric correction

Multispectral satellite image

Assesment of the quality of atmospheric correction

R rsw1

Selection of appropriate NN

chl < 5 sm < 5 doc < 5

Yes Yes YesNo No No

C chl C sm C doc

R rsw

2

Definitive decision on the adequacy of retrieval results

Concentration vector for each pixel

C sm

ii Caa *

iibb Cbb *

),( brsw bafR

),,( docsmchl CCCfrswR

),,( 00 FnLfR wrsw 00 ,, FnL w

R rsw

1

R rsw1

Establishing of flags

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Remote Sensing ReflectanceRemote Sensing Reflectance

:)0(uL

),,,( R )0(

)0( b

d

u baCfE

LS

},,{ docsmchl CCCC

}...{ *1

*1 iiCaCaa

}...{ *1

*

1 jbjbb CbCbb

ni ...3,2,1

mj ...3,2,1

:ia Specific absorption coefficient for the ith water constituent

:bjb Specific backscattering coefficient for the jth water constituent

Upwelling spectral radiance at the water surface

:)0(dEDownwelling spectral irradiance at the water surface

Measured Modeled

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The Levenberg-Marquardt Multivariate The Levenberg-Marquardt Multivariate Optimization Procedure (1)Optimization Procedure (1)

= [ / ] measured reflectance at the

wavelength j (such as a measured from a satellite)

reconstructed remote sensing reflectance,

The residual between and can be computed by one of the following ways:

The multidimensional least-square solution using all wavelengths is found by minimizing the squares of the residuals:

),( bC, ba,fRrsw

),0( uL ),0( dEjS

jS jrswR

jrswjjj SRSg /)(

f g jj

( ) ( )C C 2

)},,{ docsmchl CCCC

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The Levenberg-Marquardt Multivariate The Levenberg-Marquardt Multivariate Optimization Procedure (2)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 C0. 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.

max min iii CCC

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Hydro-optical modelHydro-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 ValidationThe Algorithm Validation

Two shipborne campaigns: June and September 2003

Historical data: 1998 – 2004(GLERL, EEGLE)

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Validation Data CollectionValidation Data Collection

Satlantic Optical In Water Profiler

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Sampling Sites in the Vicinity ofSampling Sites in the Vicinity ofKalamazoo RiverKalamazoo River

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).

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Jul-A Jul-B Jul-C Sep-B

Sampling time-place

ch

l, u

g/L

RS meanchl, ug/L

In-situmean chl,ug/L

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Sampling Sites in the Vicinity ofSampling Sites in the Vicinity ofKalamazoo RiverKalamazoo River

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).

0

5

10

15

20

25

30

Jul-A Jul-B Jul-C Sep-B

Sampling time-place

do

c, m

gC

/L

RS meandoc,mgC/L

In-situmean doc,mgC/L

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Sampling Sites in the Vicinity ofSampling Sites in the Vicinity ofKalamazoo RiverKalamazoo River

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).

0

2

4

6

8

10

12

Jul-A Jul-B Jul-C Sep-B

Sampling time-place

sm, m

g/L

RS meansm, mg/L

In-situmean sm,mg/L

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Lake Michigan Characteristic FeaturesLake 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 CPAsSeasonal Variations of Retrieved CPAs24 March 199824 March 1998

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Seasonal Variations of Retrieved CPAsSeasonal Variations of Retrieved CPAs17 April 199817 April 1998

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Seasonal Variations of Retrieved CPAsSeasonal Variations of Retrieved CPAs12 July 199812 July 1998

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Seasonal Variations of Retrieved CPAsSeasonal Variations of Retrieved CPAs25 August 199825 August 1998

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Seasonal Variations of Retrieved CPAsSeasonal Variations of Retrieved CPAs28 November 199828 November 1998

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Correlation Between Southern Lake Averaged Correlation Between Southern Lake Averaged smsm and Northern Winds During Feb/March (r = 0.95)and Northern Winds During Feb/March (r = 0.95)

2002

20032001

20002004

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 2 4 6 8 10 12number of days in February and March with strong northern winds

sm c

once

ntra

tion

QuickSAT data

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Correlation Between Southern Lake Averaged Correlation Between Southern Lake Averaged smsm and Surface Temperature in August (r = 0.85)and Surface Temperature in August (r = 0.85)

2001

1999

2002

2003

2000

1998

0

0.5

1

1.5

2

2.5

3

20.5 21 21.5 22 22.5 23 23.5

average water surface temperature, C

sm,

mg

/l

AVHRR Pathfinder data

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Monthly Variation of Area Averaged sm and doc during Spring Monthly Variation of Area Averaged sm and doc during Spring Episodic Event for 1998 in Southern Lake MichiganEpisodic Event for 1998 in Southern Lake Michigan

0

0.5

1

1.5

2

2.5

3

3.5

4

11/26/97 01/25/98 03/26/98 05/25/98 07/24/98

Date

SM

(m

g/L)

, D

OC

(m

gC/L

)

DOC

SM

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Spatial Distribution of (a) sm surface concentration, and (b)Spatial Distribution of (a) sm surface concentration, and (b) the sm Voluminal Content Per Square Kilometerthe sm Voluminal Content Per Square Kilometer

  Within along shoreline strip(Metric tons)

Within off-shore outgrowth (Metric tons)

Value for March 24, 2004 570,000 300,000

Mean March value 570,000 420,000

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A Comparison of (A Comparison of (aa)) the Spatial Distribution the sm Voluminal the Spatial Distribution the sm Voluminal Content Per Square Kilometer, and (Content Per Square Kilometer, and (bb) the Contours (in meters) of ) the Contours (in meters) of

Bottom Sediment Accumulations Reported by Schwab Bottom Sediment Accumulations Reported by Schwab et alet al..

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A Comparison of Time Variations in doc and River A Comparison of Time Variations in doc and River Discharge for Grand River through 1998-2003Discharge for Grand River through 1998-2003

0

2000

4000

6000

8000

10000

12000Jan-9

8

Jun-9

8

Nov-9

8

Apr-

99

Sep-9

9

Feb-0

0

Jul-00

Dec-0

0

May-0

1

Oct-

01

Mar-

02

Aug-0

2

Jan-0

3

Jun-0

3

date

river

dis

ch

arg

e

0

0.5

1

1.5

2

2.5

3

3.5

do

c

river discharge doc

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Climate ChangeClimate Change

Remote sensing in the visible as a companion tool for lake monitoring

Climatechange

scenario

Lakereaction

Changesobservationsfrom space

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Climate Change Scenarios for Lake Michigan: Major Ecological Climate Change Scenarios for Lake Michigan: Major Ecological Consequences and Potential Identification from SpaceConsequences and Potential Identification from Space

Climate change

scenario

Initial lake reaction

Ensuing lake reaction

Changes in observed CPAs

Major ecologic consequence

Increasein air temperature

Increase of depth-averaged water temperature

Decreaseof 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 sm concentration, decrease of doc content

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 Climate Change Scenarios for Lake Michigan: Major Ecological Consequences and Potential Identification from SpaceConsequences and Potential Identification from Space

Climate change

scenario

Initial lake reaction

Ensuing lake reaction

Changes in observed CPAs

Major ecologic consequence

Decrease of atmospheric precipitation

Decrease of river discharge

Decrease of input of sm and allochtonic doc, decrease of water turbidity in coastal zone

Decrease of sm and doc concentrations, increase of photic depth in coastal zone

Alterations of chl vertical profile, intensification of deep-layer chl, alterations of bacterial activityin coastal zone

Increase of atmospheric precipitation

Increase of river discharge

Increase of input of sm and allochtonic doc, increase of water turbidityin coastal zone

Increase of sm and doc concentrations, decrease of photic depth in coastal zone

Alterations of chl vertical profile, depletion ofdeep-layer chl, alterations of bacterial activityin coastal zone

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Future StepsFuture 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 InformationFurther 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 aBio-optical Algorithm for Lake Michigan using SeaWiFS:a Seven-year Interannual Analysis. Journal of Great Lakes Research. (in press, expected June 2006)

Contact Email:Robert.Shuchman@altarum.org