ALGAE BLOOM DETECTION IN THE BALTIC SEA WITH MERIS DATA

Transcription

1 P P German P ALGAE BLOOM DETECTION IN THE BALTIC SEA WITH MERIS DATA ABSTRACT Harald Krawczyk P P, Kerstin Ebert P P, Andreas Neumann P Aerospace Centre Institute of Remote Sensing Technology, Rutherford Street Berlin, Germany, HTUharald.krawczyk@dlr.deUTH, HTUkerstin.ebert@dlr.deUTH, HTUandreas.neumann@dlr.deUTH Satellite remote sensing is an effective tool for the regular observation of the ecological state of the environment. The availability of MERIS radiances in the visible and near-infrared parts of the spectrum gives the useful opportunity of regular observation the water quality in coastal waters. Therefore the development and validation of appropriate interpretation algorithms is necessary. A special case-2 interpretation algorithm, developed for the Baltic Sea, will be presented. The Algorithm is basing on atmospheric corrected MERIS reflectances and estimates the concentrations of Phytoplankton, inorganic Sediments and Yellow Substance. The algorithm - a model based inversion/regression technique - will be introduced and demonstrated on examples in the Baltic Sea. 1. PCI - AN INVERSION METHOD FOR MULTISPECTRAL REMOTE SENSING DATA Case 2 waters are characterized by a number of optically active independent constituents which are affecting the measured reflectance spectrum by their specific absorption and scattering properties. Additionally to case-1 water, the open ocean, which is mainly determined by the Pigment concentration, one finds inorganic sediments or suspensions and dissolved organic matter (Gelbstoff). All these components are influencing the entire spectrum and a change in the signal in one channel cannot be uniquely assigned to one component. Therefore classical colour ratio algorithms for the determination of concentrations of water constituents are not simply applicable. Together with instruments covering a broad spectral range - a new class of algorithms is necessary for the interpretation of the complex case-2 environment. The problem of interpretation of remote sensing data is a problem of matching between the geophysical parameters (Chlorophyll, Sediment, Gelbstoff) and the measured optical radiation spectra. R ( θ,φ ) 1 R ( θ,φ ) 2 R ( θ,φ ) 3 R ( θ,φ ) 4 R ( θ,φ ) n G A Weighting matrix 1 Weighting matrix 2 Weighting matrix k Fig. 1 Principle of the Inversion of Ocean Colour Data C S Y Fig. 1 shows a general interpretation scheme for remote sensing data. R denotes the radiance or reflectance in the different channels. G and A denote the geometrical information like sun and observer geometry and some auxiliary information, location, season etc. The values are collected and numerically processed in the weighting matrix boxes and finally give the desired geophysical parameter values. The concrete content of the matrix boxes determines the fashion of the algorithm. If e.g. two channels are divided we will get a colour algorithm. The boxes can also contain Neural Networks. The main problem in the development of the interpretation algorithm is the determination of the weighting matrix boxes. The algorithm which shall be here introduced, the so called Principal Component Interpretation algorithm (PCI), belongs to the model based interpretation methods. A physical-mathematical model is used to establish a relation between the geophysical concentration set p and the remote sensed radiation field R. [1] The map p R consists of a nonlinear model together with a radiative transfer code and the necessary inherent optical properties of the matter, as there are the wavelength-specific absorption and scattering coefficients and the scattering phase functions. Then the inverse mapping R p is the desired interpretation. The problem is to find an effective method for the inversion of the direct, known mapping. Neural Networks are one opportunity. The here used PCI approach goes a different way. The main idea is to estimate the concentration as a linear function of the measured reflectances. But this assumption initially contradicts the nonlinear character of the direct model. Two steps were taken to improve the situation. Firstly instead of the direct parameter p a semi logarithmic approach q = p +.1 log(p) is

2 used, secondly, the interesting parameter range of p is divided into sub-ranges, where the linear assumption is much better ustified. Now we assume an estimation approach q = p +.1 log(p) = + n ( r ) ki R b = 1 The coefficients kbib and offsets bb Bare to be determined for each sub-range r from the model, n is the number of spectral channels. The estimation shall be optimal in a global sense, i.e. the RMS error of the entire dataset should be minimized. This is a difference to so called direct model inversion methods, which try to minimize every individual spectrum by finding the optimal concentration set for a given model and single spectrum. Here is used a locally linear regression technique to estimate the needed coefficients. In this task the inversion of the regression matrix of reflectances is a necessary step. Due to the high spectral correlations this can lead to massive numerical errors. Therefore a regularisation method must be applied, to overcome this ill-posed problem. One also must take into account the always present measurement noise. As an optimal information extraction and noise suppression tool, the principal component analysis (PCA) was chosen. The determination of the interpretation coefficients roughly follows the scheme in fig 2. Step 2: Principal Component Analysis Step 3: Intrinsic Dimensionality D=max(k) with >>1 λ k Step 4: Reverse Correlation p^ p i i im m = C PC for MSE min σi m λm Step 5: Reverse Tranformation to L ^ D p p U ( L L ) D U ( L L ) i i 1 l 2 = Cp 1 + Cp σ L λ L λ p Step 1: Modeling L( λ)=f(c, S, Y, τ) = 1 = 1 Step 6: Determination of Coefficients for the Linear Estimator $p = k L + C i i i In the first step a large data set of reflectances R or radiances L according to the model is simulated. Then a principal component analysis is performed. The eigenvalues λ determine the intrinsic dimensionality, i.e. the principal components corresponding to the highest eigenvalues contain the main and useful part of information and the lower eigenvalues corresponding components contain the measurement noise. A correlation between geophysical parameters and high principal components is established (step 4). This is simple, since the principle components are orthogonal. But his formula can not yet be used for a general interpretation, because the result of PCA strongly depends on the statistical (covariance) properties of the initial data set. The data set in a natural environment can never be expected the same, as that used for the simulation. Therefore this formula must be generalized. This can be done by backtransforming the principal components to radiances using the eigenvectors. Finally one gets a regression formula between parameters and radiances (step 5 and step 6). Comparing with a Neural Network approach one could asses, both methods are performing a model inversion, minimizing the global interpretation error and differing mainly in the method of training the interpretation coefficient sets. Neural nets are often using backpropagation techniques, PCI uses principal component transformation as an optimized error-noise suppressing filter. One advantage of the PCI is the additional information about correlations between the parameters and principal components, which allows a direct estimation of the interpretation potential of the investigated data set. Concerning the piecewise linearization of the data set during interpretation one has to choose the appropriate of the pre-calculated

3 coefficient sets. The problem is solved by trying all sets and testing the sub-range conditions under which they were calculated. In the case of impossibility to find a solution a flag is set. 2. BALTIC SEA MODEL The content of the model - the specific properties of the considered geophysical parameters - significantly influences the calculation of the coefficient sets. Therefore an adequate model of the observed environment is of high importance for a good quality of the interpretation results. Since the investigations are focussed on the detection of algae blooms in the Baltic Sea in the following the properties of the used water model shall be described specific Pigment absorbtion specific sediment absorption fig. 3 Baltic Sea Model - specific properties Baltic 2 Station p14an_27.rve z = 1m Gran Canaria z = 1m specific Pigment scattering sediment single scattering albedo.98 Baltic Station p14b_23.rve z = 1m model measurement fig. 4 intercomparison of model and measurements The base model was developed in the Institute for Baltic Sea Research, by Dr. Herbert Siegel. It starts from the basic parameters Chlorophyll and Gelbstoff concentrations and the total suspended matter, characterized by the backscattering coefficient b b at 663nm, and gives their specific optical properties. This model was transformed into the more commonly used 3 component C-S-Y model, [2] where C is again the Chlorophyll concentration measured in [µg/l], Y is the Gelbstoff described by its absorption value at 44nm a y [44nm] and S is the concentration of inorganic matter given as the scattering coefficient at 55nm b S [55nm]. For a complete description and use in radiative transfer equations a number of additional parameters is necessary, the Gelbstoff slope and the sediment Angstroem coefficient for the wavelength behaviour of absorption and. scattering and the scattering phase functions for Chlorophyll and Sediments or in simplified models the relative backscattering coefficients.. Fig. 3 shows the specific wavelength dependency of the scattering and absorption properties for pigment and sediment. Fig. 4 demonstrates the practicability of the proposed model. In situ measurements of volume reflectances in the Baltic Sea during a ship campaign in 2 are compared with model calculations. There are shown two case 2 water examples with a signal decrease in the blue what points to increased Gelbstoff concentrations. One sees a very good agreement, except a wavelength range about 68nm. In the blue curves one sees a slight peak due to chlorophyll fluorescence, which is not included in the current model. The Gran Canaria curve is a clear water spectrum with low chlorophyll, to demonstrate the correctness of the model approach also for case APPLICATION EXAMPLES The PCI algorithm was realized for MERIS level 2 data. Input parameters are the atmospheric corrected bottom water leaving reflectances under consideration of the complete angular dependence of the radiation field. That means that the sun and observer geometry are exactly included in the radiative transfer model. A prototype exists which transforms level 2 MERIS data to maps of the geophysical parameters Chlorophyll, Gelbstoff and Sediment. Fig. 5 shows the occurrence of an algae bloom in summer 23. The area between the islands Bornholm and Ruegen in the South Baltic are shown in a MERIS reduced resolution scene from One clearly observes the increased concentration patterns North West off the coast of Rügen.

4 Another interesting question is the comparison of the geophysical products obtained by different algorithms. This is a critical question because one can never expect a complete agreement. The assumptions of the algorithms and the used models are too different to get identical results. But it makes sense to intercompare the results on a statistical base. For this the PCI products were opposed the standard MERIS level 2 products algae-2 concentration Gelbstoff and total suspended matter calculated on a Neural Network approach [3]. For this a scene from the was chosen pointing at the Bay of Gdansk in the South Baltic at the coast of Poland. In that time an in-situ measurement from our department campaign took part, and there was a one day later overflight of the MOS device [4], a multispectral imaging spectrometer developed in the DLR. Fig. 6-8 show the accordant geophysical parameters of the MERIS standard algorithm, the PCI for MERIS and the PCI for MOS results. In fig. 9 there are compared the histograms of the parameters. The patterns are quite similar, and as one can see from the histograms, the concentrations of all parameters have the same order of magnitude. MERIS - RR MER_RR 2PNPDK23197_9254_223218_165_7233_274_South_baltic1.N1 Pigment Sediment Gelbstoff [µg/l] (c) hk MERIS-RR-1973_south.cdr Fig. 5 Meris PCI-products during an algae bloom summer 23 in the South Baltic Comparison Pigment Algae-2 MERIS-PCI MOS-PCI 1 1 [µg/l] Fig.6 Comparison of the pigment products 1 1 [µg/l] 1 1 [µg/l]

5 Comparison Sediment TSM MERIS-PCI MOS-PCI [mg/l] Fig.7 Comparison of the sediment products Intercomparison Yellow Substance Yellow Substance MERIS-PCI MOS-PCI.1 1 Fig.8 Comparison of the Gelbstoff products

6 Frequency Histogram-Comparison Pigment Suspended Matter Frequency Lg (c[µg/l]) Gelbstoff Lg (S[mg/l]) Frequency Meris Standard-Product Meris PCI MOS PCI Lg (ay) Fig.9 Comparison of the histogramsof geophysical products 4. Summary The applicability of the proposed algorithm for the interpretation of the ecological state of complex case 2water could be demonstrated on the example of the Baltic Sea. MERIS level 2 data (atmospheric corrected water leaving radiances) can be used to detect and follow algae blooms. Intercomparisons of different algorithms and instruments show a reasonable accordance of the obtained results. For a better qualitative estimation of the new method regarding the interpreted results, a detailed analysis of in-situ measurements must be included. The validation of the products needs a detailed investigation of the relation between the theoretical models and the measured satellite and ground truth data. 5. REFERENCES 1.Krawczyk, H.; Neumann, A.; Hetscher, M. Mathematical and physical background of principal component inversion Proceedings 3 rd International Workshop on MOS-IRS and Ocean Colour, pp , Wissenschaft und Technik Verlag Berlin, 1999, ISBN Sathyendranath S., Morel A., Prieur M.: " A three component model of ocean color and its application to remote sensing of phytoplankton pigments in coastal waters", Int. J. of Remote Sensing, Vol 1, pp , (1989) 3. Doerffer R. et.al.: Pigment Index, Sediment and Gelbstoff Retrieval from Directional Water Leaving Radiance Reflectance Using Inverse Modeling Technique, MERIS-ESL Doc. No. PO-TN-MEL-GS-5, GKSS Zimmermann G., Neumann A.: The Imaging Spectrometer Experiment MOS on IRS-P3 Three Years of Experience, J. of Spacecraft Technology, Vol 1 (2), pp. 1-9