Remote Sensing of Phytoplankton for Inland Waters · 2012-07-12 · for Inland Waters Kaishan Song, Lin Li, Zuchuan Li and Linhai Li Department of Earth Sciences Indiana University

Remote Sensing of Phytoplankton for Inland Waters

Kaishan Song, Lin Li, Zuchuan Li and Linhai Li

Department of Earth SciencesIndiana University - Purdue University Indianapolis

Workshop for Remote Sensing of Coastal and Inland WatersJune 20-22, 2012

Part I: Estimation of chlorophyll-a and phycocyaninconcentrations through adaptive spectral modeling

Part II: Mapping phytoplankton size fraction with multispectral remote sensing data

This work is supported by NASA Energy and Water Cycle program (NNX09AU87G).

Public Health◦ Toxins Microcystin Cylindrospermopsin Anatoxin-a

◦ Alter taste and odor of drinking water Ecological Effects◦ Fish kills ◦ Additional effects

1.1. Introduction-Impacts of Cyanobacteria

0

0.01

0.02

0.03

400 500 600 700 800

Ref

lect

ance

(sr-1

)

Wavelength (nm)

Chl-a: 675 nm

PC: 620 nm

1.2. Objectives and Datasets

In situ datasets (Spectra, Chl-a, PC, TSM, ISM)◦ Three Central Indian reservoirs (CIN), 2005-2008, 2010

◦ Shitoukoumen Reservoir in Northeast (STKR), 2006-2008, 2010

◦ Three drinking water supplies in South Australia (SA), 2009

◦ Lake Taihu in East China (LTH), 2008-2009

Estimating Chl-a and PC through remotely sensed data

Developing algorithms to deal with the effect of ISM and/or CDOM

1.3. Modeling Approach

A: GA-PLS B: PLS-ANN

1.4. Results– Chl-a Estimate via GA-PLS

0 50 100 150 200 250 3000

50

100

150

200

250

300

Measured Chl-a (g/L)

Pred

icte

d C

hl-a

( g/

L)

(a). CIN

y = 0.981 x + 1.17 RMSE = 11.9

rRMSE =20.02 MAE = 7.54

CalibrationValidation

0 20 40 60 800

10

20

30

40

50

60

70

80


Pred

icte

d C

hl-a

( g/

L)

(b). SA

y = 1.02 x - 0.323 RMSE = 1.17

rRMSE = 5.87 MAE = 0.90


0 20 40 60 80 1000

20

40

60

80

100


Pred

icte

d C

hl-a

( g/

L)

(c). LTH

y = 1.15 x - 3.31 RMSE = 6.17

rRMSE = 30.10 MAE = 4.31


0 10 20 30 40 500

10

20

30

40

50


Pred

icte

d C

hl-a

( g/

L)

(d). STKR

y = 0.93 x + 2.203 RMSE = 4.02

rRMSE = 29.13 MAE = 3.11


0 50 100 150 200 2500

50

100

150

200

250


Pred

icte

d C

hl-a

( g/

L)

(a). MERIS-CAL

y = 0.92 x + 2.779 R2 = 0.888

N = 546

CINSALTHSTKR

0 50 100 150 200 2500

50

100

150

200

250


Pred

icte

d C

hl-a

( g/

L)

(b). Hyperion-CAL

y = 0.949 x + 2.309 R2 = 0.907

N = 546

CINSALTHSTKR

0 50 100 150 200 250 3000

50

100

150

200

250

300


Pred

icte

d C

hl-a

( g/

L)

(c). MERIS-VAL

CINSALTHSTKR

y = 0.903 x + 4.72 RMSE = 13.41rRMSE = 31.27MAE = 8.45

0 50 100 150 200 250 3000

50

100

150

200

250

300


Pred

icte

d C

hl-a

( g/

L)

(d). Hyperion-VAL

CINSALTHSTKR

y = 0.946 x + 2.99 RMSE = 12.58 rRMSE = 29.25MAE = 8.71N = 547

0 20 40 60 80 1000

20

40

60

80

100


Pred

icte

d C

hl-a

(g

/L) (a). NON-CIN

y = 0.91 x + 2.44 R2 = 0.851

N = 427

Calibration

0 50 100 150 200 2500

50

100

150

200

250

300

Measured Chl-a (g/L)Pr

edic

ted

Chl

-a (g

/L) (b). CIN

y = 0.67 x + 14.2 RMSE = 25.2

rRMSE = 40.1%MAE = 18.3

Validation

0 50 100 150 200 250 3000

50

100

150

200

250

300


Pred

icte

d C

hl-a

(g

/L) (c). NON-SA

y = 0.81 x + 8.51 R2 = 0.808

N = 1033

Calibration

0 20 40 60 800

20

40

60

80


Pred

icte

d C

hl-a

(g

/L) (d). SA

y = 1.13 x - 5.22 RMSE = 3.19

rRMSE = 17.1%MAE = 2.73

Valibration

0 50 100 150 200 250 3000

50

100

150

200

250

300


Pred

icte

d C

hl-a

(g

/L) (e). NON-LTH

y = 0.82 x + 8.03 R2 = 0.818

N = 994

Calibration

0 50 100 1500

50

100

150


Pred

icte

d C

hl-a

(g

/L) (f). LTH

y = 1.06 x -4.24 RMSE = 12.03

rRMSE = 40.82 MAE = 6.9

Validation

0 100 200 3000

50

100

150

200

250

300

Measured Chl-a (g/L)Pr

edic

ted

Chl

-a (g

/L) (g). NON-STKR

y = 0.786 x + 10.08 R2 = 0.786

N = 831

Calidation

0 20 40 600

10

20

30

40

50

60

70


Pred

icte

d C

hl-a

(g

/L) (h). STKR

y = 0.79 x + 12.36RMSE = 10.4

rRMSE = 62.1%MAE = 8.8

N = 262

Validation

1.4. Results– Chl-a Estimate via GA-PLS

0 5 10 150

100

200

300

400

500

Relative error

ISM

(mg/

L)

(a). RE vs. ISM

CINSALTHSTKR

0 5 10 150

50

100

150

200

250

Relative error

ISM

:Chl

-a ra

tio

(b). RE vs. ISM:Chl-a

y = 14.62 x-1.377 R2 = 0.756N = 952

100ˆ

i

ii

yyy

RE

-0.2 0 0.2 0.4 0.60

30

60

90

120

150

Chl

-a (

g/L)

[R-1(655)-R-1(708)*R(753)]

(a). NON-CIN-CAL

y = 161.5x+16.16R2 = 0.623N = 427

0 50 100 150 200 250 3000

50

100

150

200

250

300


Pred

icte

d C

hl-a

(g

/L) (b). CIN-VAL

y = 0.44x+16.28RMSE = 28.7 rRMSE = 49.5MAE = 19.4N = 666

0 0.2 0.4 0.6 0.80

50

100

150

200

250

300

Chl

-a (

g/L)

[R-1(655)-R-1(708)*R(753)]

(c). NON-SA-CAL

y = 247.14x+17.62R2 = 0.756N = 1033

0 20 40 60 800

20

40

60

80

Pred

icte

d C

hl-a

(g

/L)


(d). SA-VAL

y = 0.878x+0.08RMSE = 3.9rRMSE = 19.8MAE = 3.11N = 60

0 0.2 0.4 0.6 0.80

50

100

150

200

250

300

Chl

-a (

g/L)

[R-1(655)-R-1(708)*R(753)]

(e). NON-LTH-CAL

y = 253.3x+16.8R2 = 0.759N = 994

0 20 40 60 80 1000

20

40

60

80

100

Pred

icte

d C

hl-a

(g

/L)


(f). LTH-VAL

y = 0.996x-4.07RMSE = 13.22rRMSE = 47.2MAE = 8.8N = 105

-0.5 0 0.5 10

50

100

150

200

250

300

Chl

-a (

g/L)

[R-1(655)-R-1(708)*R(753)]

(g). NON-STKR-CAL

y = 242.24x+19.74R2 = 0.734N = 831

0 10 20 30 40 500

10

20

30

40

50

Pred

icte

d C

hl-a

(g

/L)


(h). STKR-VAL

y = 0.81x + 12.1RMSE = 11.7rRMSE = 77.3MAE = 9.6N = 262

1.4. Results– Chl-a Estimate via TBM

1.5. Results– PC estimation via PLS-ANN

0 100 200 300 4000

100

200

300

400

Measured PC (g/L)

Pred

icte

d PC

( g/

L)(a). ANN

y = 0.945 x + 3.602 R2 = 0.913

N = 631

MWUS-ASDMWUS-OOSA-OO

0 100 200 300 400-200

-150

-100

-50

0

50

100

150

Measured PC(g/L)

Res

idua

l (PC

pred

-PC

mea

s( g/

L))

(b). Errors

y = -0.982 x + 70.1 R2 = 0.0813


0 100 200 300 400

0

100

200

300

400

Measured PC (g/L)

Pred

icte

d PC

( g/

L)

(c). TBA

y = 0.841 x + 12.69 R2 = 0.809

N = 631


0 100 200 300 400-200

-150

-100

-50

0

50

100

150

Measured PC (g/L)

Res

idua

l (PC

pred

-PC

mea

s ( g/

L))

(d). Errors

y = -0.96 x + 69 R2 = 0.176


0 100 200 300 4000

100

200

300

400

Measured PC (g/L)

Pred

icte

d PC

( g/

L)

(a). MERIS

y = 0.849 x + 9.822 R2 = 0.84

N = 631


0 100 200 300 4000

100

200

300

400

Measured PC (g/L)

Pred

icte

d PC

( g/

L)(b). Hyperion

y = 0.935 x + 4.12 R2 = 0.901

N = 631


0 100 200 300 400

0

100

200

300

400

Measured PC (g/L)

Pred

icte

d PC

( g/

L)

(c) MERIS

y = 0.691 x + 23.26 R2 = 0.649

N = 631


0 100 200 300 400

0

100

200

300

400

Measured PC (g/L)

Pred

icte

d (

g/L)

(d) Hyperion

y = 0.745 x + 20.24 R2 = 0.701

N = 631


1.6. Chl-a and PC estimates via semi-analytical model

Simis et al.This study

GA-PLS and PLS-ANN perform more reliably in our study, and can partially compensate nonlinearity;

CDOM and ISM are key factors affecting Chl-a and PC estimates for inland waters;

More studies on inherent optical properties are needed for both coastal and inland waters.

1.7. Conclusions

2.1. Why Phytoplankton Size Fraction?

Biological pump◦ Production◦ Particle fluxParticle size

Settling speed

Particle flux

• Pico-plankton (<2um)• Nano-plankton (2~20um)• Micro-plankton (20~200um)

2.2. Optical Response

• Challenges– Phytoplankton size is a second-order variable

influencing the remote sensing reflectance

SeaWiFS satellite data◦ 6 bands◦ 9km◦ 1997-2010

MODIS satellite data◦ 10 bands◦ 9km◦ 2002-2011

Phytoplankton size fraction◦ 616 (SeaWiFS)◦ 592 (MODIS)

SeaWiFS MODIS

2.3. Data Sets

2.4. Methods

Spectral data

1. Remote sensing reflectance2. Normalized remote sensing reflectance with integration3. Band ratios4. Continuum removed spectra5. Curvature

Phytoplankton size fraction

Pico-planktonNano-planktonMicro-plankton

Support vector machine

Hydrolight simulated spectraSeaWiFS SpectraMODIS Spectra

2.5. Results–Simulated and imagery data sets

res R2 Slope Intercept RMSE

0.70 0.71 0.18 0.14

0.94 0.85 0.07 0.07

0.94 0.86 0.06 0.07

7) 0.93 0.86 0.06 0.07

res R2 Slope Intercept RMSE

0.62 0.65 0.11 0.18

0.70 0.69 0.10 0.16

0.67 0.74 0.08 0.17

7) 0.66 0.75 0.08 0.17

ico Nano Micro Reference

58 0.60 0.70 This study

-- -- 0.60 Mouw and Yoder (2010). JGR.

o-

2.6. Results–Phytoplankton Size mapping

o-

-

2.7. Results-MODIS Data Setature mber

R2 Slope Intercept RMSE

5 0.66 0.68 0.17 0.18

30 0.80 0.80 0.10 0.13

00 0.81 0.81 0.10 0.13

15 0.70 0.69 0.09 0.11

30 0.72 0.74 0.08 0.10

50 0.73 0.75 0.08 0.10

00 0.74 0.78 0.06 0.10

15 0.63 0.63 0.09 0.12

30 0.73 0.72 0.07 0.11

50 0.76 0.81 0.06 0.10

00 0.74 0.84 0.04 0.10

Micro

Nano

Pico

2.8. Results–Great Lakes

Micro-plankton fraction

Nano-plankton fraction

Pico-plankton fraction

SeaWiFS (June 2006)

2.8. Results–Temperature vs PSF

Lake Erie Lake Superior

2.9. Conclusions and Future Work

Phytoplankton size fraction has been derived from multi-spectral remote sensing data

Continuum removed spectra and curvature are the most important spectral parameters

Examining the correlation of phytoplankton size to nutrients and climatic parameters

Investigating the effect of climate change on phytoplankton functional groups

Documents

Remote Sensing of Phytoplankton for Inland Waters · 2012-07-12 · for Inland Waters Kaishan Song, Lin Li, Zuchuan Li and Linhai Li Department of Earth Sciences Indiana University