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

www.altarum.org

2

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

www.altarum.org

3

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

www.altarum.org

4

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

www.altarum.org

5

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

www.altarum.org

6

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

www.altarum.org

7

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

www.altarum.org

8

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.

www.altarum.org

9

The Algorithm Validation

  Two shipborne campaigns: June and September 2003 Historical data: 1998 – 2004 (GLERL, EEGLE)

www.altarum.org

10

Validation Data Collection

 Satlantic Optical In Water Profiler

www.altarum.org

11

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).

www.altarum.org

12

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).

www.altarum.org

13

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).

www.altarum.org

14

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

www.altarum.org

15

Seasonal Variations of Retrieved CPAs 24 March 1998

www.altarum.org

16

Seasonal Variations of Retrieved CPAs 17 April 1998

www.altarum.org

17

Seasonal Variations of Retrieved CPAs 12 July 1998

www.altarum.org

18

Seasonal Variations of Retrieved CPAs 25 August 1998

www.altarum.org

19

Seasonal Variations of Retrieved CPAs 28 November 1998

www.altarum.org

20

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

www.altarum.org

21

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

www.altarum.org

1998 23.5

22

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

www.altarum.org

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

www.altarum.org

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.

www.altarum.org

25

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

www.altarum.org

date

26

Climate change scenario

Climate Change

Remote sensing in the visible as a companion tool for lake monitoring Lake reaction Changes observations from space

www.altarum.org

27

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

www.altarum.org

28

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

www.altarum.org

29

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.

www.altarum.org

30

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]

www.altarum.org

31