Imaging spectrometry to estimate chlorophyll-a in Lake Garda
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Imaging spectrometry to estimate chlorophyll-a in Lake Garda
G. Candiania, C. Giardinoa, N. Strömbeckb and E. Ziliolia
a
CNR-IREA, Remote Sensing Dep., Milan, Italy, email: giardino.c@irea.cnr.it
b
Dep. of Limnology, EBC, Uppsala University, Uppsala, Sweden
ABSTRACT
On 13th July 2000, an aerial over flight was accomplished by DLR on Lake Garda, providing high resolution
imagery from ROSIS, to be used for estimation of chlorophyll-a concentrations in the south-eastern portion of the
basin. In coincidence with sensor overpasses, water samples were collected in ten pelagic stations for subsequent
laboratory analyses of water quality parameters. Image data were corrected by atmospheric effects using a radiative
transfer code and measured values of aerosol optical depth. The atmospherically corrected reflectances were then
used to describe the variation of chlorophyll-a content in the lake by means of semi-empirical approaches. The best
performing algorithm (R2=0.77), was obtained with the 498 nm/610 nm band ratio which estimated chlorophyll-a
with RMSE=0.5 µg/l. The results confirmed capabilities of ROSIS to map water quality parameters in inland
waters. In order to evaluate performances of bio-optical modelling, the matrix inversions method was applied on
measured GER spectra corrected by sunglint and skyglint. The SIOPs used in the model came from waters whose
optical properties were assumed similar to those of Lake Garda. The standard error in estimating chlorophyll-a
concentrations was 0.3 µg/l. This exercise gave promising results for the application of matrix inversion methods
to ROSIS imagery to retrieve chlorophyll-a concentrations.
Keywords: ROSIS, chlorophyll, lake water.
1 INTRODUCTION
Water is the lifeblood of the environment, essential to the survival of all living things. Although water covers
nearly three quarters of the earth’s surface in oceans as well as rivers, lakes, snow and glaciers, less than five per
cent of this total exists as freshwater. Freshwater resources are vital for meeting basic needs and inadequate
protection of the quality of freshwater resources can set important limits to sustainable development. Therefore,
water pollution represents a major global environmental problem. Once polluted, the water body requires a
considerable amount of time and money to clear of pollutants and restore the water quality. In order to prevent the
worsening of water condition, continual water quality monitoring is hence required. The major water quality
parameters that have to be monitored include: chlorophyll-a (Chl-a), suspended particulate matter (SPM), and
coloured dissolved organic matter (CDOM).
Traditionally, water quality has been assessed using limnological methods and laboratory analyses of samples
from the field. Determining water quality using field-collected samples is time consuming and expensive, and the
issue of whether the limited number of field data can adequately represent the overall quality of a vast water body is
often disputed. To overcome the disadvantages of traditional data-collection methods, exploitation of remote
sensing data for water quality assessment has been largely investigated. [1, 2, 3, 4] have shown that remote sensing
data can be used to estimate certain individual water quality parameters.
However, the spectral resolution of satellite sensors could be still to coarse for local-scale monitoring and/or
spectral bands not enough narrow to sense little variations of water quality parameters which are optically actives.
Instead, aerial imaging spectrometry represents a powerful tool for those applications, providing also the chance to
learn best configurations for future space-borne sensors.
In this study, the potentials of the aerial Reflective Optics System Imaging Spectrometer (ROSIS) sensor have
been evaluated for mapping Chl-a concentrations in Lake Garda during summer, using remote sensing-inferred
methods, including semi-empirical band-ratio algorithms and bio-optical analytical models.
Presented at the 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May 2003
3892 MATERIALS AND METHODS
2.1 The study area
Lake Garda is located in the sub-alpine district, where several other lakes and rivers give the region more than the
50% of all Italian freshwater resources. Likely the other lakes in the same district, it was formed by fluvial erosion
processes after the glacial action during all the Pleistocene. With an area of 368 km2 and a water volume of 49 km3,
it is the largest freshwater basin in Italy and one of the most important lakes of the European Union and it needs
accurate care due to its natural relevance, besides the economical importance because of the tourist–related
activities.
The lake is oligomitic with summer stratification of its water between June and October. The chlorophyll
concentrations range from 0.2 µg/l to 10 µg/l, the Secchi disk depths from 4-5m in summer to 15-17 m in late
winter, and the suspended solid concentrations between 0.2 mg/l and 4.5 mg/l [5, 6]. In accordance with the
classification proposed by the Organisation for Economic Co-operation and Development (OECD) [7] Lake Garda
is oligo-mesotrophic, whereas relating with terminology used by oceanographers, its water is more similar to Case-I
than Case-II.
2.2 In situ data
A ground-truth campaign was accomplished in the south-eastern basin of Lake Garda, in coincidence to the
aerial overflight to the aim of calibrate and/or validate image data, and modelling.
Sun-photometric data were measured for estimating the aerosol content in the atmosphere to calibrate the
radiative transfer model to be used to remove atmospheric signals from ROSIS data. The model was calibrated also
using meteorological hour-based data collected by local ground stations, and related to wind intensity and direction,
air temperature and humidity, and solar irradiance.
During the over flight, the boat was stopped at several positions to collect the first integrated meter of water for
subsequent laboratory analyses of Chl-a and SPM. Concentrations of Chl-a ranged from 4.23 to 7.4 µg/l, while the
changing of SPM was from 1.25 to 1.88 mg/l. Moreover, it was observed that the inorganic component of the
suspended matter (i.e. SPIM) was smaller than 10%. The Secchi disk depths ranged from 3.7 m to 4.7 m.
In addition to water sampling, measurements of remote sensing reflectance Rrs (=Lw(0+)/Ed(0+)), using a GER
1500 spectroradiometer were also performed. Firstly the downwelling irradiance Ed(0+) was measured using a
spectralon reference panel, and then the upwelling radiance Lw(0+) was measured by pointing the sensor at nadir
angle at the water surface. A Li-Cor sensor placed on the fore-deck of the boat was used for continuos monitoring
of the downwelling quantum irradiance in the PAR region. This was then used for correcting the radiometric
measurements for changes in irradiance levels.
2.2.1 Radiometric corrections
The measurements of Lw(0+) contained both sunglint and skyglint. For correction of this, firstly the model
proposed by [8], was used to separate Ed(0+) direct solar irradiance and diffuse sky irradiance (continental
atmosphere and a visibility of 50 km, Lat 47.3° N, local solar time). Then the model by [9] as interpreted by [10]
was used to correct the upwelling radiance for sun-and skyglint. According to [10], the used model could be used
with care for Case-2 water without a heavy load of suspended matter. However, since the reference wavelengths
proposed (765 or 865 nm) resulted in negative reflectances in the near infrared, the average minimum wavelength
of the measured spectra, 745 nm, was used. The correction procedures gave estimates of the radiance reflectance
above water, Rr(0+) (=Lu(0+)/Ed(0+)). For modelling purposes, the spectra were finally transferred to below the
surface, Rr(0-). This was done by multiplying Rr(0+) with 1.815 for radiance focusing of the upwelling radiance,
and dividing by 0.96 for surface reflectance of the downwelling irradiance [11].
2.3 Image data
On 13th July 2000, within the framework of EC Hy-Sens-2000 EC Project [12], the Reflective Optics System
Imaging Spectrometer (ROSIS) onboard of the DLR Do228 research aircraft, acquired hyperspectral images of
Lake Garda with ground resolutions about 2.5×2.5 m2.
Synchronous to the aerial survey, the vertical atmospheric transmittance was estimated using the MS-120 EKO
Sun-photometer. By separating these estimates into the different contributions made by the atmospheric
constituents, the particulate-matter optical thickness was evaluated according to the Langely method [13]. The
particulate-matter optical thickness at 550 nm, along with the aerosol percentage compositions achieved from
390historical data, was used in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) [14] code to
remove the atmospheric effect from ROSIS data, according to parameters listed in Tab. 1.
Table 1. Specification of ROSIS and atmospheric parameters
Spectral range 440-820 nm
FWHM 7.5 nm for all channels
Altitude of lake surface 60 m
Flight altitude 4560 m above sea level
Flight heading 345°
Sun azimuth/zenith angles 146°/27°
Atmosphere Mid latitude summer
Aerosol model 40% dust-like, 44% water-soluble, 5% oceanic, 11% soot
3 RESULTS
3.1 Conversion of ROSIS measurements to chlorophyll-a concentrations
A semi-empirical approach, in which atmospherically corrected reflectance values are regressed against in situ
measurements, was used to convert ROSIS measurements into Chl-a concentrations. To the aim, 5 by 5 pixel sized
regions, have been selected from imagery in correspondence of the limnological stations. The correlation analyses
have been made by means of single band and the band ratio algorithms.
474
486
498
510
522
534
546
558
570
582
594
606
618
630
642
654
666
678
690
702
714
726
738
750
762
774
786
798
810
822
834
846
858
870
874
462 Correlation coeff. R
474
486
498
510
522 1.0 to 0.8
534
546
558 0.8 to 0.6
570
582
594 0.6 to 0.4
606
618
630 0.4 to 0.2
642
654
666
0.2 to 0.0
678
690 0.0
702
714
726 0.0 to -0.2
738
750
762 -0.2 to -0.4
774
786
798 -0.4 to -0.6
810
822
834
-0.6 to -0.8
846
858 -0.8 to -1.0
870
874
Figure 1. The correlation matrix shows the best combination of ROSIS measurements to include in a band-ratio algorithm to
estimate the chlorophyll-a concentrations in Lake Garda. For instance, the cell (1,1) shows the correlation coefficient between
the chlorophyll-a and the 462 nm /474 reflectance ratio.
391The single band approach has been used to find the correlation degree between the Chl-a concentrations and
reflectance values at lake surface level, measured in all single ROSIS channels. The maximum values of Pearson
coefficients r was 0.59, found in channel 1 (417 nm).
The use of channel ratios to determine relationships between image data and water quality parameters is usually
adopted because it normalize some of the effects of measurement geometry and atmosphere [1, 15]. The ratio of
each of the visible and near-infrared ROSIS channels against each other was hence calculated. These ratios were
linearly correlated with the ten chlorophyll-a concentrations, allowing the identification of any channel ratios that
showed a high correlation with chlorophyll-a. The value of the Pearson correlation coefficient, between the ratio of
each pair of channels and the concentrations of chlorophyll-a was displayed as a matrix (Fig. 1). Low values of
correlation coefficients show that the radiance values at these wavelengths are not influenced by the presence of
phytoplankton. The bands in the visible regions explained up to the 80% of the variation in chlorophyll-a
concentrations, with a maximum for the 498 nm/610 nm ratio (88%).
3.1.1 The bio-optical modelling
In an analytical approach, the optical properties of the water column are physically related to the subsurface
irradiance reflectance, and thence to the water-leaving radiance, and thence to the satellite-received radiance. The
analytical method involves the inversion of this three-step approach, to determine water quality parameters from
image data. If the last step is explained by radiative transfer models, such the 6S code used in this study, the other
tasks are explained by bio-optical models. [16] accurately described a bio-optical model predicting radiance
reflectance just below the water surface as a function of chlorophyll-a, phaeophytin-a, CDOM, and SPM, both
organic (SPOM) and inorganic (SPIM). Once the model is calibrated by means of the Specific Inherent Optical
Properties (SIOPs) of the water body, together with the f factor describing the anisotropy of the light field into the
water column, it can be used to predict the water quality parameters from radiance reflectance values. One of the
fastest approach for estimating water quality parameters from the subsurface radiance reflectance is given by the
matrix inversion method [17-18].
During the time of the ROSIS over flight, the SIOPs of Lake Garda waters were not yet measured. In order to
run the bio-optical model described in [16], we used the SIOPs of an oligotrophic Swedish lake. Then,
radiometrically corrected (cf. par. 2.2.1) subsurface radiance reflectance measured in situ and reseampled according
to the ROSIS Full Width Half Maximum (FWHM), were used as input in the matrix inversion method, giving
estimations of Chl-a concentrations. Those values were compared to in situ measurements. Running the inversion,
it was observed that chlorophyll estimations were very sensitive to the spectral interval of measured subsurface
radiance reflectances, given as input to the matrix inversion. Therefore, for each station, the matrix inversion was
run changing the spectral range, until estimations of chlorophyll concentrations were reasonably close to measured
values, as shown in Tab. 2. We did not discuss the reason why Chl-a estimations were so dependent on
wavelengths chosen, also because SIOPs used in the model were not the actual values of Lake Garda.
Table 2. Specification of wavelength ranges selected in each station for modelling Chl-a; measured values are also listed
Station Spectral range Chl-a modelled Chl-a measured
1 420-700 4.8 4.2
2 600-700 7.4 7.1
3 550-700 7.1 6.9
4 550-700 7.1 7.1
5 460-700 6.1 6.1
6 650-700 7.3 7.4
7 650-700 6.9 7.4
8 430-700 5.6 5.8
9 420-700 5.7 5.9
10 500-700 5.9 5.8
3924 DISCUSSION AND CONCLUSIONS
This study showed the potentiality of imaging spectrometry in estimating the chlorophyll-a concentrations in inland
waters. ROSIS imagery were acquired over Lake Garda, in the north of Italy, over an area in which the difference
between the maximum and the minimum value of measured chlorophyll-a was only 3 µg/l. Band ratio algorithms,
which normalise atmospheric and geometric noises, gave good results in describing those variations of chlorophyll-
a. All the bands centred around 500 nm, rationed by bands centred around 600 nm, explained about the 80% of
chlorophyll-a changing, and the best correlation (R2=0.77) was given by the 498 nm/610 nm ratio.
Even if the results were statistically good and, generally speaking, the regression analyses between remote
sensing data and point measurements allow to map water quality parameters all over the surface, quickly and
rapidly, this approach always need lake-truth data. Moreover the equations relating ROSIS to chlorophyll-a in this
study, can not be applied to other lakes and/or environmental conditions.
The way forward to wholly use the potentials of ROSIS and, more generally, EO data in operational monitoring
programmes of freshwater resources, would be the exploitation of a physically-based approach by which the optical
properties of the water column become physically related to the subsurface reflectance, and thence to the water-
leaving radiance, and finally to the ROSIS-received radiance. Inverting this approach means to correct data by the
atmospheric and air/water interface effects, and estimating concentrations of water quality parameters from
subsurface radiance reflectance, by knowing the SIOPs of the lake waters, and by calibrating a bio-optical
analytical model.
In this study, this possibility was analysed by using subsurface radiance reflectance values estimated from GER
spectra measured above the water and SIOPs values measured in Swedish lake waters. Those data were used as
input in a matrix inversion method (i.e., least squares regression obtained by minimising the sum of squared
residuals) and modelled Chl-a concentrations were compared to measured values. The standard error in estimating
chlorophyll-a from matrix inversion over 10 stations was 0.3 µg/l, while a strong dependency of those estimations
from spectral ranges was observed. However, results of modelling and matrix inversion methods were encouraging
and they would be reasonably applied to ROSIS data. In any case, the usefulness of this methodology have to be
always subordinate by measurements of inherent optical properties in Lake Garda waters.
ACKNOWLEDGMENTS
The ROSIS data were collected by the Deutschen Zentrum für Luft-und Raumfahrt (DLR) within the framework of
HySens-2000 Project. None of these results would have been possible without the contribution of CNR-ISDGM of
Venice and Agenzia Proviciale per la Protezione dell’Ambiente of Trento. This study was co-funded by the
Agenzia Spaziale Italiana (ASI).
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