MONITORING WATER QUALITY OF THE PERIALPINE ITALIAN LAKE GARDA THROUGH

MULTI-TEMPORAL MERIS DATA 

Gabriele Candiani(1), Dana Floricioiu(2), Claudia Giardino(1), Helmut Rott(2)

 

(1) Optical Remote Sensing Group-IREA, National Research Council, via Bassini 15, I-20133 Milan, Italy

(2) Institute of Meteorology and Geophysics, University of Innsbruck, Innrain 52,A-6020 Innsbruck, Austria

Objective

This work represents ongoing research efforts aimed at developing remote sensing strategies which

address problems of water quality in Lake Garda

Parameterisation of a bio-optical model

Time-series of FR MERIS data

L1P-derived chl-a concentrations vs L2P-Algal2 vs in situ data

The distribution of water resources

Earth29%

71%

Land

Water

Hydrosphere97%

3%

Saltwater

Freshwater

Italian lakes

2%

2%

11%

85%

Islands South Centre North

(Mosello & Salmaso, 2005)

Freshwater

68.9%

29.9%

0.9%

0.3%

Ice

Groundwater

Humidity soil/atmosphere

Surface water (lakes &rivers)

Study area

Lake Garda is the largest Italian lake and one of the most important lake of the European region and needs accurate care for its natural relevance and its importance due tourism, drinking water, water supply, irrigation and recreation.

The lake was chosen because the EO-related activity has a pretty long tradition at this lake and optical properties are well-studied.

The lake dimension is in agreement with the pixel size of FR MERIS data.

WQP N Average St. Dev. Distributionchl-a 450 3.52 2.73 LognormalSPM 131 1.61 0.99 Lognormal

aCDOM(440) 51 0.14 0.13 Lognormal

Oligo-mesotrophic state (OECD)

Lake area 368 km2 Volume 49.03 km3 Maximum depth 350 m Average depth 133 m Maximum width 16 km

The MERIS FR dataset (1/2)

AO553 & AO164 projects ESA PI projects

Chlorophyll-a is the only parameter that is routinely measured by the 3 local environmental protection agencies

in charge for Laka Garda monitoring.

Atmospheric correction of L1P FR data

L1P TOA radiances

6S code (with AOT550 measured in situ or estimated from imagery using the Dark Dense Vegetation approach (Floricioiu & Rott, poster)

22 Jul 03

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

412 462 512 562 612 662 712 762

Wavelengths [nm]

Rrs

[sr-1

]

In situ PR-650

MERIS(AOT550=0.28,v=0.48)

RrsMERIS R6S/

The MERIS FR dataset (2/2)

The bio-optical model (1/5)

440,,, CDOMCDOMNAPw aaSPMaachlaaa

achlachlAachla B

,Wavelength A B

[nm] [-] [-]400 0.055 0.429425 0.076 0.453450 0.077 0.517475 0.068 0.633500 0.055 0.599525 0.030 0.469550 0.019 0.111575 0.012 -0.192600 0.008 0.152625 0.011 0.114650 0.013 0.190675 0.035 0.387700 0.006 0.155725 0.001 0.769750 0.000 1.024

440440, NAPSNAPNAP eaSPMa

440440440, CDOMSCDOMCDOMCDOM eaaa

Phytoplankton absorption

0.00

0.10

0.20

0.30

0.40

400 450 500 550 600 650 700 750

Wavelength [nm]

a [m

-1]

0.5 1.0 2.0 5.0 10.0

N=22

440,,, CDOMCDOMNAPw aaSPMaachlaaa

S NAP

N 22Min. 0.0050Max. 0.0130

Average 0.0079Std. Dev. 0.0026

y = 0.0227x

0.000

0.020

0.040

0.060

0.080

0.0 1.0 2.0 3.0 4.0

SPM [g m-3]

a NA

P(4

40

) [m

-1]

0.000.020.040.060.080.100.12

400 450 500 550 600 650 700 750

Wavelength [nm]

a NA

P [m

-1]

0.2 0.5 1.0 2.0 5.0

Non-algal particles absorption

The bio-optical model (2/5)

achlachlAachla B

,

440440, NAPSNAPNAP eaSPMa

440440440, CDOMSCDOMCDOMCDOM eaaa

440,,, CDOMCDOMNAPw aaSPMaachlaaa

S CDOM

N 47Min. 0.0097Max. 0.0320

Average 0.0190Std. Dev. 0.0053

0.00

0.20

0.40

0.60

0.80

400 450 500 550 600 650 700 750

Wavelength [nm]

a CD

OM

[m-1

]

0.02 0.05 0.10 0.20 0.50

CDOM absorption

The bio-optical model (3/5)

achlachlAachla B

,

440440, NAPSNAPNAP eaSPMa

440440440, CDOMSCDOMCDOMCDOM eaaa

y = 0.0067x-0.3824

0.0000.0050.0100.0150.0200.0250.0300.035

0.0 0.1 0.2 0.3 0.4

aCDOM(440) [m-1]

SC

DO

M [n

m-1

]

SPMbachlbbb bSPIMbbwb ,,

achlachlbb

515.0091.0, SPIMSPMbbSPIM 1384.00398.0,

The bio-optical model (4/5)

Backscattering coefficients

y = 0.7722x

0

1

2

3

4

5

0 1 2 3 4 5 6

SPM [g m-3]

SP

IM [g

m-3

]

0.00

0.02

0.04

0.06

0.08

400 450 500 550 600 650 700 750

Wavelength [nm]

bbS

PIM

[m-1

] 0.2 0.5 1.0 2.0 5.0

0.00

0.01

0.02

0.03

0.04

0.05

400 450 500 550 600 650 700 750

Wavelength [nm]

bb

[m-1

]

0.5 1.0 2.0 5.0 10.0

b

b

ba

bu

2

10guggg

Hrs

Brs

gggggg R

Raverage lnminmin

210210 ,,,,

uu..R .Brs 192108800450

ugRBrs

0.0%

0.5%

1.0%

1.5%

0.0% 0.5% 1.0% 1.5%

RrsH() [sr-1]

Rrs

()

[sr-1

]

The bio-optical model (5/5)

Parameterisation using HYDROLIGHT

Lee Z, Carder K. L., Mobley C. D., Steward R. G. and Patch J. S., Hyperspectral remote sensing for shallow waters. I. A semianalytical model, Applied Optics, 37, 6329-6338, 1998.

MERIS & bio-optical modelling (1/2)

05

1015

202530

0.0

2

0.1

2

0.2

1

0.3

1

0.4

0

0.5

0

0.6

0

0.6

9

aCDOM(440) [m-1]

Fre

qu

en

cy

0.000.100.200.30

0.400.500.60

Pro

ba

bili

ty

020406080

100120

0.1

01

.90

3.7

15

.51

7.3

29

.12

10

.92

12

.73

14

.53

16

.34

18

.14

chl-a [mgm-3]

Fre

qu

en

cy

0.00

0.05

0.10

0.15

0.20

0.25

Pro

ba

bili

ty

0

10

20

30

40

50

0.1

00

.59

1.0

81

.57

2.0

62

.55

3.0

53

.54

4.0

34

.52

5.0

15

.50

SPM [gm-3]

Fre

qu

en

cy

0.000.05

0.100.15

0.200.25

0.30

Pro

ba

bili

ty

Probability Density Function

WQP Distribution Average St. Dev.Chl-a Lognormal 1.02 0.70SPM Lognormal 0.30 0.65aCDOM(440) Lognormal -2.28 0.75

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

400 450 500 550 600 650 700 750

Wavelength [nm]

Rrs

()

[sr-1

]

Forward bio-optical modelling

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

400 450 500 550 600 650 700 750

Wavelength [nm]

Rrs

()

[sr-1

]

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

400 450 500 550 600 650 700 750

04 Jun 03

19 Jun 03

06 Jul 03

22 Jul 03

07 Aug 03

19 May 04

15 Jul 04

31 Jul 04

13 Aug 04

Inversion of MERIS Rrs data

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

B12

B13

B14

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

B12

B13

B140.8-1.0

0.6-0.8

0.4-0.6

0.2-0.4

0.0-0.2

Band ratio (br)

79.1

7

56.21

BR

BRachl

M

M

rs

rs

Optimization (opt)

Mrs

Brs

aSPMachl R

Raverage

CDOM

lnmin440,,

MERIS & bio-optical modelling (2/2)

Results (1/2)

0

3

6

9

12

15

04

-Ju

n-0

3

19

-Ju

n-0

3

06

-Ju

l-03

22

-Ju

l-03

07

-Au

g-0

3

19

-Ma

y-0

4

15

-Ju

l-04

31

-Ju

l-04

13

-Au

g-0

4

chl-a

[mg

m-3

]

In situ br opt Algal2

Evaluation of L1P-derived chl-a productsAnabaena bloom at the surfaceIn

situ

dat

a on

ly in

the

nort

hern

par

t

RMSE [mgm-3] = 1.20 0.87 1.60 In situ chl-a are the average values from

measurements provided by the 3 agencies

L1P chl-a & Algal2 products show

0

3

6

9

12>12

chl-

a [m

gm

-3]

Results (2/2)

Algal2 L1P-derived

Conclusions and future work

Preliminary results obtained from 6S corrected L1P FR MERIS data are promising to implement a in situ-independent method to assess chl-a concentration in Lake Garda (RMSE < 1 mgm-3 with the optimisation method using B4 to B9).

On the average L2P data give also good results but the spatial information is minor due to the presence of masked pixels.

More images, acquired as close as possible to field data, are necessary to verify the method to invert Rrs spectrum (using br algorithms, opt techniques, others?) or the accuracy of Algal2 products (and of L1P irradiance-reflectance products).

The effect of SIOPs on chl-a assessment had to be better understood.

New data of Lake Garda waters are going to be collected to increase the knowledge on optical properties and to verify the optical closure of the bio-optical model with in situ measured Rrs values.

Acknowledgements

MERIS data were supplied by ESA (AO553 and AO164 PI projects)

In situ data were provided by APPA Trento, ARPAV Veneto and ASL-Brescia

We are very grateful to A. G. Dekker, V. E. Brando & N. Strömbeck for the continuous support on our researches on Lake Garda

This work was co-funded by Agenzia Spaziale Italiana

Thank you very much for you attention