remote sensing of phytoplankton for inland waters · 2012-07-12 · for inland waters kaishan song,...
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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
0 20 40 60 80 1000
20
40
60
80
100
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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
Measured Chl-a (g/L)
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)
Measured Chl-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)
Measured Chl-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)
Measured Chl-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
MWUS-ASDMWUS-OOSA-OO
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
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))
(d). Errors
y = -0.96 x + 69 R2 = 0.176
MWUS-ASDMWUS-OOSA-OO
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
CalibrationValidation
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
CalibrationValidation
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
CalibrationValidation
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
CalibrationValidation
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