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

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 χ² 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.

Acknowledgements

This work received supported under the ADEOS‐II/GLI Project of the Japan Aerospace Exploration Agency (JAXA), the Academic Frontier Promotion Project of the Ministry of Education, Science, Sports, and Culture of Japan, the 973 Project of the People's Republic of China (Project Number 2003CB415205), and the National Natural Science Foundation of China (Project Number 40471088).