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«Year: 1998 Endmember selection procedures for partial spectral unmixing of DAIS 7915 imaging spectrometer data in highly vegetated Areas Kneubühler, ...»

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Values lower than 0 are excluded as well. These endmembers are used in a second unmixing step. This process can be repeated until a certain “purity-criteria” like no negative abundances or all abundances between 0 and 1 is achieved. Fig. 6 shows the differences between a one-step spectral unmixing of sugar beet and a two-step unmixing based on iteratively determined purer endmembers from imaging data.

FIGURE 6: One-step (left) and two-step (right) spectral unmixing of sugar beet, based on image derived endmembers The evaluated methods indicate that ground truth collection is important to validate the different approaches of endmember selection. Correlating field measurements of biochemical and biophysical parameters to imaging data can offer the possibility to retrieve such parameters from imaging data. The approach using in-situ reference measurements of endmember spectra and the modelling approach using a SVAT Model may be more promising since image derived endmembers are made up of a spectral mixture of contributing elements, unless pure pixels are iteratively determined.

Nevertheless, it remains difficult to define representative endmember spectra. Modelling endmember spectra bases on the availability of a range of parameters describing the status of the plant and the architecture of the canopy, which itself is subject to strong BRDF effects. Applications in vegetation studies have to take into account these effects, since airborne imaging spectrometers with large swath angles produce data of high geometric complexity.

4 REFERENCES [1] OERTEL, D., 1994: “The High Resolution Imaging Spectrometer DAIS: a Status Report”, in: 2nd EARSEC Status Seminar, ed. A.J. Sieber, JRC Ispra [2] SCHAEPMAN, M., KNEUBUEHLER, M., MEIER, E., MUELLER,SCHLÄPFER, D., STROBL, P., REULKE, R. AND HORN, R., 1998: “Fusion of Hyperspectral (DAIS 7915), Wide-angle (WAAC), and SAR (E-SAR) Data Acquisition Methods - The MultiSwiss’97 Campaign”, Proc. SPIE, 3438 [3] SCHAEPMAN, M., 1998: “Calibration of a Field Spectroradiometer - Calibration and Characterization of a Non-Imaging Field Spectroradiometer Supporting Imaging Spectrometer Validation and Hyperspectral Sensor Modelling”, PhD thesis, Dept. of Geography, Univ. of Zurich, p.146 [4] RICHTER, R., 1996: “Atmospheric correction of DAIS hyperspectral image data”, Computers and Geosciences, 22: 785-793 [5] SCHLÄPFER, D.R., 1998: “Differential Absorption Methodology for Imaging Spectroscopy of Atmospheric Water Vapor”, PhD thesis, Dept. of Geography, Univ. of Zurich, p. 131 [6] ENDSLEY, N. H., 1995: “Spectral unmixing algorithms based on statistical models”, Proc. SPIE, 2480: 23-36 [7] VERDEBOUT, J., JACQUEMOUD, S. AND SCHMUCK, G., 1994: “Optical properties of leaves: modelling and experimental studies”, in: Imaging Spectroscopy - a Tool for Environmental Obs., eds. Hill, J. and Mégier, J., Euro Courses, Kluwer, 4: 169-191 [8] JACQUEMOUD, S. AND BARET, F.,1990: “PROSPECT: a model of leaf optical properties spectra”, Rem. Sens. Env. 34: 75-91 [9] VERHOEF, W., 1984: “Light scattering by leaf layers with application to canopy reflectance modelling: the SAIL model”, Rem. Sens. Env. 16: 125-141 [10] CLEVERS, J.G.P.W., 1994: “Imaging spectrometry in agriculture - plant vitality and yield indicators”, in: Imaging Spectroscopy - a Tool for Environmental Obs., eds. Hill, J.

and Mégier, J., Euro Courses, Kluwer, 4: 193-219 [11] SCHLÄPFER, D., MEYER P. AND ITTEN, K.I., 1998: “Parametric Geocoding of AVIRIS Data Using a Ground Control Point Derived Flightpath”, Summaries of the Seventh JPL Airborne Earth Science Workshop, (in press), JPL, Pasadena (CA), pp. 6 [12] BOARDMAN, J.W., 1990: “Inversion of high spectral resolution data”, Proc. SPIE, 1298: 222-233 [13] RSI Inc., 1997: “ENVI User’s Manual”, Vers. 3.0, Lafayette [14] SCHANZER, D.L., 1993: “Comments on ‘The Least Squares Mixing Models to Generate Fraction Images Derived for Remote Sensing Multispectral Data’“, TGARS, 31: 747



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