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The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal\udMODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which\udprovides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain\udco-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a\udclassification target pixel and each classification classes. The global land cover classification experiments have been conducted by\udapplying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance) and NBAR(Nadir\udBRDF-Adjusted Reflectance) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As\udthe results, SR and NBAR products showed similar classification accuracy of 99%

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS