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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 - PowerPoint PPT Presentation
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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