Water quality monitoring in the Baltic Sea is of high ecological importance for all its neighbouring countries. They are
highly interested in a regular monitoring of water quality parameters of their regional zones. A special attention is paid to
the occurrence and dissemination of algae blooms. Among the appearing blooms the possibly toxicological or harmful
cyanobacteria cultures are a special case of investigation, due to their specific optical properties and due to the negative
influence on the ecological state of the aquatic system. Satellite remote sensing, with its high temporal and spatial
resolution opportunities, allows the frequent observations of large areas of the Baltic Sea with special focus on its two
seasonal algae blooms. For a better monitoring of the cyanobacteria dominated summer blooms, adapted algorithms are
needed which take into account the special optical properties of blue-green algae. Chlorophyll-a standard algorithms
typically fail in a correct recognition of these occurrences.
To significantly improve the opportunities of observation and propagation of the cyanobacteria blooms, the Marine
Remote Sensing group of DLR has started the development of a model based inversion algorithm that includes a four
component bio-optical water model for Case2 waters, which extends the commonly calculated parameter set chlorophyll,
Suspended Matter and CDOM with an additional parameter for the estimation of phycocyanin absorption. It was
necessary to carry out detailed optical laboratory measurements with different cyanobacteria cultures, occurring in the
Baltic Sea, for the generation of a specific bio-optical model.
The inversion of satellite remote sensing data is based on an artificial Neural Network technique. This is a model based
multivariate non-linear inversion approach. The specifically designed Neural Network is trained with a comprehensive
dataset of simulated reflectance values taking into account the laboratory obtained specific optical properties of the algae
species, according to the wavelengths of MERIS VIS/NIR bands. The input to the inversion neural network are
atmospheric corrected (Level2) MERIS bottom of atmosphere reflectances as well as viewing geometries of the sensor
from which the output maps for chlorophyll concentration, Suspended Matter concentration, CDOM absorption and
phycocyanin absorption are generated.
The paper demonstrates the theoretical basis and development of the algorithm together with a number of example
results obtained from MERIS scenes in the Baltic Sea. Furthermore it compares the phycocyanin-algorithm with the
standard DLR PCI algorithm based on the related inversion technique "Principal Component Analysis" and discusses the
different inversion approaches.
Subject of the paper is the presentation of the potential of use of multispectral remote sensing data for the investigation
of water quality of large water basins on the example of the monitoring of the Baltic Sea with MERIS data. An
interpretation and inversion scheme for optical satellite data over water has been developed to be used in several national
and international projects to monitor different aspects of water quality. The resulting "Principal Component
Interpretation" algorithm allows an optimized estimation of water constituents: chlorophyll pigment concentrations,
suspended matter concentration and yellow substance concentration as well as optical properties of the water body. From
these are derived secondary parameters like water transparency. In the frame of the international ESA MARCOAST
project this interpretation scheme was developed for a regular (daily) monitoring of the Baltic Sea. Results are uniformly
mapped images and concentration maps of the Baltic Sea area from which are additionally derived weekly, monthly and
seasonal means. The Principal Component Interpretation belongs to the class of model based multivariate interpretation
schemes and is closely related to Neural Networks techniques, but bases on a completely different training procedure. It
makes use of an optimal information redistribution between the spectral bands and relates them to the water constituents.
This kind of estimation allows an simultaneous estimation of expected global estimation accuracy. The regular
monitoring is accompanied by the survey of in-situ ground measurements, which can be used for validation.The paper
will present the bio-optical model which is used for the interpretation of Baltic Sea water.
The basics of the interpretation scheme basing on principal component analysis will be explained and results of the
monitoring of different products will be discussed on examples of a time series in 2008, showing the development and
movement of algae blooms, together with other constituents. The obtained results are critically compared with available
ground measurement.
In March 1996 the German Aerospace Agency successfully launched an imaging spectrometer on the Indian IRS-P3 satellite. The Modular Optical Scanner (MOS) is a pushbroom scanner. It was designed for the investigation of the atmosphere ocean system, to gather information about the state of the water body in consideration of the atmospheric influence. Coastal zones are of special interest, because of the presence of different classes of water-constituents. The combination of chlorophyll, sediments and yellow substance (C,S,Y) is characteristic for case-2 waters which are of high importance for recent ecological problems. The main problem for remote sensing is the interpretation of the satellite radiances in terms of geophysical quantities. This paper introduces a model based inversion technique, using principal component analysis as a tool for optimal information extraction for multispectral radiance data. The parameters of interest are estimated as linear combinations of measured radiances. A new aspect of the algorithm is that the atmosphere and water parameters are treated equally throughout the procedure, so that no extra atmospheric correction procedure is required.
Satellite remote sensing of atmospheric properties is important for investigation of atmospheric pollution and also for remote sensing of the underlying surface, where an atmospheric correction is needed. For the proof of new methodological concepts the multispectral imaging spectrometer MOS was developed in the DLR Institute of Space Sensor Technology and launched on the Indian satellite IRS- P3. It has 13 bands in the VIS/NIR region with 10nm bandwidth. MOS successfully provides data for more than 2 years over European and Northern African coasts. The paper will introduce a standard atmospheric correction scheme for MOS data over water regions using measurements in the near IR form 685 nm to 1000 nm. This method is based on a 2- channel correction, estimating the aerosol optical depth and the Angstrom coefficient for the spectral behavior of the optical thickness. After extrapolation of the visible region the atmospheric correction is applied. Examples will be shown from the Baltic and North Sea regions. The obtained result will be compared and discussed with available in situ measurements taken simultaneously with MOS overflights. Lastly, this algorithm is applied to an observation of forest fire smoke over Malaysia.
The difficulty of the remote sensing of coastal water is the presence of more than one constituent with high variability ranges, different correlation and spectral behavior. They are superimposing in their influence on the resulting total spectrum. Simple ratio algorithms applied to remote sensing data fail on the quantitative determination of the single constituents. However, coastal regions are of great interest for remote sensing since most of the consequences of urbanization are manifested here. For the improvement of remote sensing of coastal zones it is not only necessary to build a new generation of sensors that offer spectrally higher resolved data, but one has to develop a new methodology that allows the separation and determination of the water constituents based on the entire spectral signature of the different components of the water body. The imaging spectrometer MOS flying on the Indian remote sensing satellite IRS-P3 provides since March 1996 remote sensing data in 13 spectral channels for the scientific community. We implemented a new methodological approach to derive different case II water constituents as well as atmospheric turbidity for the application of MOS-data in costal regions. A new point of the method is the uniform consideration of atmospheric and water constituent influences on the remote sensing signal. The paper will present a short overview on the algorithm's essentials and examples for the large variability of coastal waters around Europe basing on the results of the retrieved water constituents using the MOS algorithm. It will demonstrate the promising potential of this new algorithm for discrimination of single constituents under case II conditions. Derived maps of chlorophyll like pigments, sediments and aerosol optical thickness are shown and will be discussed.
DLR's imaging spectrometer the Modular Optoelectronic Scanner (MOS) on the Indian remote sensing satellite IRS-P3 has been orbiting since March 1996. MOS consists of two spectrometers, one narrow band spectrometer around 760 nm for retrieval of atmospheric parameters and a second one in the IVS/NIR region with an additional line camera at 1,6 micrometers . The instrument was especially designed for the remote sensing of coastal zone water and the determination and distinction of its constituents. MOS was developed and manufactured at the Institute of Space Sensor Technology (ISST) and launched in a joint effort with the Indian Space Research Organization (ISRO). The high spectral resolution of MOS offers the possibility of using the differences in spectral signatures of remote sensing objects for quantitative determination of geophysical parameters. In ISST a linear estimator to derive water constituents and aerosol optical thickness has been developed, exploiting Principal Component Inversion (PCI) of modeled top-of- atmosphere and experimental radiance data sets. The estimator results in sets of weighting coefficients for each measurement band, depending on the geophysical situations. Because of systematic misinterpretation due to non- adequateness of model and real situation the further development implies the parallel improvement of used water models and recalibration with in-situ data. The paper will present for selected test sites of the European coasts results of algorithm application. It will show the improvement of the estimated water constituents by using regional specific model parameter. Derived maps of chlorophyll like pigments, sediments and aerosol optical thickness ar presented.
In the Institute for Space Sensor Technology a new generation of remote sensing imaging spectrometers was developed, measuring the reflected from the ocean atmosphere system radiance in the visible to near-infrared spectral range. This Modular Optical Scanner was successfully launched on 21 March 1996 with an Indian satellite to a polar sunsynchronous orbit, and on 23 April 1996 with the Russian Priroda Module on the MIR station. For the purpose of interpretation of these measurements over oceans and coastal zones has been developed a special algorithm based on Principal Component Analysis, using a special inversion technique for a given ocean-atmosphere physical mode. An important question in the description of such models are the inherent optical properties of the water. In the paper will be given a description of the derivation of the interpretation algorithm for different water constituents, with an inherent atmospheric correction. It will be shown how specific optical properties are influencing the interpretation results. This work was performed in cooperation with the Baltic Sea Research Institute Warnemuende.
Subject of the paper is the investigation of the information content of high dimensional multispectral remote-sensing measurements in the VIS-NIR region for ocean-atmosphere problems. The final goal of such measurements is the separation of atmospheric influence and the retrieval of detailed information of water constituents. Primary questions appearing during interpretation process are: how many independent parameters can be found from the measurements? what is the physical sense of these parameters? what is the accuracy of the parameters? One has to take into account that the properties of the measuring device, like channel position, bandwidth, number of channels and measurement accuracy have a great influence on the interpretation. One possible method to get answers to the above questions is the Principle Component Analysis (PCA). A problem in PCA is the physical interpretation of the mathematically obtained results - Eigenvalue, Eigenvector and Principle Components. Because the results of PCA interpretation depend on the statistical properties of the measurement data, they must be mapped back to the absolute measurement quantities (radiances). To get a physical interpretation of the PCA results a detailed investigation with a simulated data set using a simplified (but nonlinear) model was realized (atmosphere after Gordon, Sturm, water reflectances after Sathyendranath, Morel, Prieur). It will be presented a concept, how in-situ measurements can be involved into interpretation model with PCA.
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