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Assessment of the universal pattern decomposition method using MODIS and ETM data

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Abstract:

The universal pattern decomposition method (UPDM) is a sensor-independent method in which each satellite pixel is expressed as the linear sum of fixed, standard spectral patterns for water, vegetation and soil. The same normalized spectral patterns can be used for different solar-reflected spectral satellite sensors. Supplementary patterns are included when necessary. The UPDM has been applied successfully to simulated data for Landsat/ETM+, Terra/MODIS, ADEOS-II/GLI and 92-band CONTINUE sensors using ground-measured data. This study validates the UPDM using MODIS and ETM+ data acquired over the Three Gorges region of China. The reduced 2 values for selected area D, that with the smallest terrain influences, are 0.000409 (MODIS) and 0.000181 (ETM+), and the average linear regression factor between MODIS and ETM+ is 1.0077, with root mean square (rms) value 0.0082. The linear regression factor for the vegetation index based on the UPDM (VIUPD) between MODIS and ETM+ data for area D is 1.0089 with rms 0.0696. Both UPDM coefficients and VIUPD are sensor independent for the above sensors.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160600617228

Affiliations: 1: National Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China 2: Laboratory of Nature Information Science, Department of Information and Computer Sciences, Nara Industrial University, Nara, Japan 3: Laboratory of Nature Information Science, Department of Information and Computer Sciences, Nara Women's University, Nara, Japan 4: Laboratory of Nature Information Science, Department of Information and Computer Sciences, Nara Women's University, Nara, Japan,KYOUSEI Science Centre for Life and Nature, Nara Women's University, Nara, Japan 5: Department of Economics, Doshisha University, Kyoto, Japan

Publication date: 2007-01-01

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