Spectral Characterisation of the Osterseen Lake District - EARSeL ...

Spectral Characterisation of the Osterseen Lake District
S. Thiemann a , A. Albert a and S. Zimmermann b
a Deutsches Zentrum für Luft- und Raumfahrt (DLR), P.O.Box 11 16, 82230 Wessling,
Germany, email: sabine.thiemann@dlr.de, andreas.albert@dlr.de
b Limnologische Station Iffeldorf, Technische Universität München
ABSTRACT
The Osterseen lake district south of Munich, Germany, consists of 19 glacially emerged single waterbodies which
are interconnected with each other via channels. There, one can find a wide spectrum of lake types, on the one hand
caused by miscellaneous ground water influxes, on the other hand because of different nutrient loadings. The
extraordinary diversity of the hydrological and chemical properties builds an ideal basis for comparative
limnological studies. The test site in the Osterseen lake district allows the investigation of spectrally and spatially
high resolution remote sensing data to determine the water quality in respect to water constituents as well as littoral
vegetation.
In June 2001, the test area was recorded by the airborne hyperspectral imaging spectrometer ROSIS to test and
adapt remote sensing algorithms for the detection of water quality parameters. Simultaneously, in situ water
samples were taken from all lakes which were analysed in laboratory regarding the concentrations of chlorophyll-a,
mineral suspended matter, and Gelbstoff.
The ROSIS data were calibrated to at-sensor radiance and atmospherically corrected. Using a semi-analytical
method the directional reflectance is modeled in the pelagial and littoral zones based on the in situ data. The results
show that more effort should be put into ROSIS stray light correction and the in situ measurement of specific
optical properties in various lakes.
Keywords: water constituents, analytical model, atmospheric correction, lake district.
1 INTRODUCTION
During the last decade an analytical model for forward and inverse calculation of water constituents in pelagial case
2 waters was developed based on the extensive in situ data base available for Lake Constance [1] [2] [3]. It utilizes
the inherent optical properties of clear water, chlorophyll, Gelbstoff, and suspended minerals. Now, this tool is
being extended by a module that accounts for the influencing signal from the bottom in the littoral zone [4].
The present study examines whether the algorithm developed for Lake Constance is transferable to other lakes with
different phytoplankton composition.
2 TEST AREA
The Osterseen lake area developed after the last glacial period and is found south of lake Starnberg in Upper
Bavaria, Germany (Fig. 1). The system consists of 20 small lakes that are connected by natural channels. The lake
system is almost exclusively fed by ground water with a small contribution from one tributary. Originally, all lakes
of the Osterseen area were oligotrophic, hard water lakes but from their genesis up to now they developed diverse.
On the one hand, all the lakes exhibit approximately the same morphology and are exposed to the same climatic
environmental factors. On the other hand, ground water input and anthropogenic pollution of the lakes vary
extremely. Therefore, within a small area are located a broad spectrum of lakes different in their hydrological and
their chemical conditions. The following lakes are representatives of different trophic states (location see Fig. 1):
Lake Waschsee (eutrophic), Lake Sengsee (mesotrophic), Lake Eishaussee (mesotrophic), and Lake Herrensee
(oligotrophic).
Presented at the 3 rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May 2003
446

3 DATA
Hamburg
Germany
Munich
Berlin
Lustsee Grobensee
N
Westl.
Breitenauersee
Härtlings Sill
Lintensee
Stechsee
Ameisensee
Östl. Breitenauersee
Ground Water
Wellspring
Großer Ostersee
Eishaussee
Brauhaussee
Herrensee
Fischkaltersee
Forchensee
Wolfelsee
Sengsee
Fohnsee
Schiffhüttensee
Waschsee
Figure 1. Location of the test area "Osterseen Lake District" south of Munich, Germany.
0 500 m
3.1. Hyperspectral remote sensing data acquired by ROSIS sensor
The Reflective Optics System Imaging Spectroradiometer (ROSIS) was developed since 1986 in cooperation
between DLR, GKSS, and MBB (now Astrium) [5][6][7]. It is a push broom scanner with 512 spatial and 115
spectral pixels recording in the wavelength range between 430 nm and 860 nm. The spectral sampling interval
amounts 4 nm and the full width at half maximum is about 7.5 nm. With an instantaneous field of view of 0.59
mrad the spatial resolution results in 2.8 x 2.8 m² at a flight altitude of 5,000 m.
The data presented in this study were recorded on June 5 th , 2001 between 10:42 and 11:20 UTC in a northerly flight
direction at an altitude of about 5,000 m. The data were radiometrically corrected based on laboratory
measurements using a calibrated light source. The geometric correction of the distortions due to flight attitude (roll,
pitch, and yaw angles) uses information from the airplane's inertial system, the flight velocity, the altitude above
ground, and the focal length of the telescope [8]. The radiometrically and geometrically corrected composit of two
flight lines is shown in Fig. 2.
447

Figure 2. Subset from two ROSIS flight lines acquired on June 5 th , 2001 - true color band composit after
radiometric calibration and geometric rectification. Points of in situ measurements and drawn spectra
448

3.2. In situ data
Simultaneously to the overflights, water samples were taken and Secchi depth was measured in each lake of the
Osterseen Lake District at 1m and 3 m water depth, and in cases of high Secchi depth also at 5 m. The samples
were analysed in the laboratory for the optically relevant water constituents chlorophyll, suspended matter, and
Gelbstoff. Chlorophyll analysis was conducted by photometric measurement of the transmission of the solution at
665 nm and 750 nm after the samples were filtered through a 1 µm filter, extracted [9], and centrifugated.
Suspended matter was measured as total, organic, and inorganic concentration. The water samples were filtered
through a dried and weighed 1 µm glass fiber filter and dried for two hours at 105°C and then for four hours at
550°C with weighing after each step. The total suspended matter is the dry weight after the first step subtracted by
the filter weight, the inorganic matter is the weight after the second step, and the organic matter is the loss between
these two weighings. For the determination of the Gelbstoff absorption, the water samples were sucked through a
0.2 µm filter. The transmission was measured with a Perkin-Elmer Lambda-2 dual beam spectrophotometer in 10
cm and 5 cm cuvettes. From these two transmission spectra the Gelbstoff absorption was determined including the
pure water correction using the absorption spectrum from [10]. The in situ data are given in Tab. 1 including also
the spectral slope to additionally characterize Gelbstoff spectrally.
Table 1. In situ measurements of water constituents on June 5 th , 2001
Lake Secchi Depth Chlorophyll-a Total suspended Gelbstoff [m
[m]
[µg/l] Matter [mg/l]
-1 ] Gelbstoff
Spectral Slope S
Waschsee [1 m] 1.9 6.2 0.9 0.62 0.0150
[3 m] 41.7 4.6 0.77 0.0138
Schiffhüttensee [1 m] 1.5 16.7 3.4 0.65 0.0133
[3 m] 22.4 4.9 0.53 0.0147
Sengsee [1 m] 3.8 2.0 1.5 - -
[3 m] 2.0 2.9 0.69 0.0142
Fohnsee [1 m] 6.9 0.5 0.6 0.64 0.0134
[3 m] 2.0 0.9 0.15 0.0114
Westl. Eishaussee [1 m] 5.0 0.9 0.7 0.52 0.0148
[3 m] 1.7 1.7 0.41 0.0132
Östl. Eishaussee [1 m] 4.9 1.2 0.5 0.43 0.0137
[3 m] 2.3 1.4 0.58 0.0148
Gr. Ostersee - S [1 m] 1.9 1.8 2.3 - -
[3 m] 1.8 3.2 0.32 0.0152
Gr. Ostersee - N [1 m] 1.5 2.2 2.2 0.41 0.0117
[3 m] 1.8 2.9 0.38 0.0125
Westl. Breitenauersee [1 m] 3.5 1.2 1.9 0.36 0.0126
[3 m] 1.2 1.9 0.56 0.0154
Lustsee [1 m] 7.5 - 0.4 0.67 0.0143
[3 m] 0.9 - 0.49 0.0139
[5 m] 0.2 1.0 - -
Stechsee [1 m] 4.5 0.7 4.1 0.57 0.0145
[3 m] 0.6 3.7 1.08 0.0116
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4 METHODS
4.1. Analytical modeling of remote sensing reflectance
The output spectra after the atmospheric correction (see chapter 4.2) are given in units of reflectance above the
surface, R + =
R rs + L. The first part represents the remote sensing reflectance of the water, and the second part the
reflectance of the water surface. The spectra measured by ROSIS are corrected for the surface effect by comparing
the infrared wavelengths with simulated spectra using the in situ measurements. The factor of the reflected sky
radiance L is obtained iterative.
The underwater remote sensing reflectance is modeled according to [4] by a set of analytical equations including
shallow water. The parameterizations were developed using simulations of the radiative transfer program
Hydrolight (version 3.1), which is explained in detail by [12]. The remote sensing reflectance below the water
surface is the sum of two parts: of the water body and of the bottom, Rrs,W and Rrs,B. These two parts can be
expressed as follows:
K
u , W R K
B
u,
B
R
rs Rrs,
W Rrs,
B f x 1
A1
exp
Kd
zB
A2
exp Kd
zB
cos
v
cos
v
with the f-factor of the remote sensing reflectance and the attenuation coefficients of the downwelling irradiance,
the upwelling irradiance of the water body, and the upwelling irradiance reflected from the bottom – Kd, Ku,W, and
Ku,B respectively:
2 3 p
5 p6
f p
1 1 p2x
p3x
p4x
1
1
,
cos
s cos
v
a bb
K d 0 ,
cos
s
1 , W
2,
W
K
u,
W a bb
1 x
1
, and
cos
s
1 , B
2,
B
K
u,
B a bb
1 x
1
.
cos
s
The parameter x is the ratio bb/(a+bb) with the total absorption a and the backscattering coefficient bb. The sun
position is given by the solar zenith angle below the water surface s, the viewing position by v. The
parameterization regards the influence of the bottom reflectance RB at the bottom depth zB. The absorption a and the
backscattering coefficient bb are calculated using a bio-optical model developed by [13] and [14] for Lake
Constance. The total absorption is the sum of the absorption of pure water, Gelbstoff, and phytoplankton. The
backscattering coefficient is determined by the backscattering of the pure water [10] and of suspended matter. The
coefficients Ai, pj, and k were obtained by a multiple regression analysis of all Hydrolight simulations using a
Marquardt-Levenberg fit. The values of the coefficients are listed in Tab. 2. The analytical equations of the remote
sensing reflectance are used for forward modeling of spectra of the lakes and for inversion of airborne data of
ROSIS as well. The inversion technique uses the Simplex algorithm after [15].
Table 2: Coefficients for the model of the remote sensing reflectance.
A1 1.1576 p1 0.0512 sr -1
A2 1.0389 sr
0 1.0546
-1
p2 4.6659 1,W 3.5421
p3 -7.8387 2,W -0.2786
p4 5.4571 1,B 2.2658
p5 0.1098 2,B 0.0577
p6 0.4021
4.2. Atmospheric correction of the ROSIS data
The ROSIS data were atmospherically corrected using the ATCOR4 program based on MODTRAN calculations
[16][17]. It accounts for the irradiation characteristics of the sun, for molecule and aerosol scattering in the
atmosphere, for the scan angle effect of additional atmospheric path with distance to nadir view, and for the
adjacency effect from the nearby environment. The necessary input parameters including altitude, specific
insolation, and atmospheric variables are shown in Tab. 3. They were considered constant for the time slot of data
take.
450

Table 3. Input parameter for atmospheric correction of the ROSIS flight lines
flight altitude [km] 5.65
ground altitude ab. sea level [km] 0.59
sun zenith angle [°] 25.2
sun azimuth angle [°] 181.1
flight heading [90°east] 351
day of the year 155
water vapor content [g cm - ²] 2.0
aerosole type rural
visibility [km] 25.0
adjacency box [Pixel] 30
By inflight calibration experiments one can generally check the validity of the laboratory calibration. The
radiometric performance of an airborne sensor may differ from the one in laboratory due to the aircraft environment
and the longer distance between the sensor and the target influencing stray light effects in the blue wavelength
range. Therefore, for the present study different calibration coefficients have been generated and tested using the
inflight calibration module in ATCOR 4 (see Fig. 3). It appeared that the resulting atmospherically corrected
reflectance spectra are very sensitive to variations with these coefficients. The best results compared to modeled
spectra of selected lakes were achieved by applying the coefficients partly derived from inflight calibration with a
shallow water area in Lake Ostersee with logarithmic interpolation between 478 nm and 730 nm (see Fig. 3 h).
Figure 4 shows an intercomparison between modeled spectra (Fig. 4) for four selected lakes (including one shallow
water spectrum modeled for 1 m water depth over sandy sediment) and atmospherically corrected ROSIS spectra of
the same location using 3 x 3 pixel mean values. The shallow water spectrum (No. 13) can be reproduced similarly
using the forward model. The deep water spectra are in the same order of magnitude, however the reflectance
between 450 nm and 550 nm is sometimes higher in the ROSIS spectra.
Arbitrary units
0.004
0.0035
0.003
0.0025
0.002
0.0015
0.001
0.0005
0
400 450 500 550 600 650 700 750 800 850
Wavelength [nm]
Figure 3. Calibration coefficients tested for atmospheric correction a) standard coefficients, b) generated from
inflight calibration at a different test site "Nantes", c) generated from inflight calibration with modeled spectrum
Lake Ostersee shallow water (Fig. 2, No. 13), d) generated from inflight calibration with modeled spectrum Lake
Fohnsee (Fig. 2, No. 7), e) generated from inflight calibration with modeled spectrum Lake Sengsee (Fig. 2, No.
10), f) joined coefficients from 3c below 450 nm and from 3b above 450 nm, g) mean from coefficients 3b and 3c,
and h) coefficients from 3c with logarithmic interpolation between 478 nm and 730 nm.
5 DATA ANALYSIS AND INTERPRETATION
For all lakes mentioned in Tab. 1, the reflectance was modeled based on the concentration of water constituents as
measured in situ (Fig. 5a). In Fig. 4, the corresponding ROSIS spectra were extracted with the mean value of a 3 x 3
pixel matrix. For some lakes like Lake Ostersee (southern basin) or Lake Breitenauer See (western basin), the
451
a
b
c
d
e
f
g
h

ROSIS spectra match quite well with the modeled spectra (see Fig. 4 a-c). However, several reasons exist why the
other ROSIS spectra (see Fig. 4 d) do not match with the modeled ones:
1. Radiometric calibration of the sensor has to be improved (stray light).
2. Atmospheric correction should be more accurately (adjacency effect, aerosol type).
3. Sunglint has to be accounted for more accurately.
4. It should be investigated how the optical properties of water constituents (phytoplankton absorption,
backscattering of suspended matter) vary from test site to test site.
5. The model may be improved including e.g. fluorescence, surface effects.
Reflectance [%]
Reflectance [%]
14
12
10
8
6
4
2
0
400 450 500 550 600 650 700 750 800 850
Wavelength [nm]
30
25
20
15
10
5
0
400 450 500 550 600 650 700 750 800 850
Wavelength [nm]
a) 14
b)
Reflectance [%]
0
400 450 500 550 600 650 700 750 800 850
Wavelength [nm]
Figure 4. Modelled (blue and orange) spectra in comparison with ROSIS (green and red) spectra for the lakes a)
Ostersee (southern basin) (see Fig. 2 No. 1), b) Breitenauersee (western basin) (see Fig. 2 No. 3), c) shallow water
with 1 m bottom depth in southern Ostersee (see Fig. 2 No. 13), and d) Sengsee (see Fig. 2 No. 10)
6 CONCLUSION
The ROSIS data from Osterseen lake district with various concentrations of water constituents show great potential
for algorithm testing – especially regarding shallow water areas. The above mentioned influencing factors will be
examined more closely in further evaluations. In future, special focus will be put on stray light correction of the
ROSIS sensor and on optical in situ measurements to obtain a more accurate calibration of the sensor. More effort
will be put on the. Also a data base of various specific optical properties will to be set up.
A processing chain to derive water constituents from any hyperspectral sensor, modular inversion program for
water (MIP-w), is under development in our group [14]. The advantage of MIP-w is to produce distribution maps
from hyperspectral remote sensing data.
12
10
8
6
4
2
c) 14
d)
Reflectance [%]
452
12
10
8
6
4
2
0
400 450 500 550 600 650 700 750 800 850
Wavelength [nm]

ACKNOWLEDGMENTS
Part of the work is done in the frame of the Special Collaborative Program 454 "Lake Constance Littoral" provided
by fundings of the German Research Foundation DFG. The in situ data were analyzed for water consitutents by
Biologiebüro Weyhmüller. Peter Gege supported us with his WASI program and the Gelbstoff analysis. We thank
Rolf Richter for providing and introducing the ATCOR program. The Hydrolight code was provided by C.D.
Mobley.
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