Remote Sensing of Phytoplankton for Inland Waters Kaishan Song, Lin Li, Zuchuan Li and Linhai Li Department of Earth Sciences Indiana University - Purdue University Indianapolis Workshop for Remote Sensing of Coastal and Inland Waters June 20-22, 2012 Part I: Estimation of chlorophyll-a and phycocyanin concentrations 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).
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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
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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
Measured Chl-a (g/L)
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
CalibrationValidation
0 20 40 60 80 1000
20
40
60
80
100
Measured Chl-a (g/L)
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
CalibrationValidation
0 10 20 30 40 500
10
20
30
40
50
Measured Chl-a (g/L)
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
CalibrationValidation
0 50 100 150 200 2500
50
100
150
200
250
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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