Normalizing Aster Data Using Modis Products for Land Cover Classification

ASTER has similar bandwidths and spatial resolution to Landsat and is an important component of the mid-resolution data archive. However, the limited duty cycle of ASTER and relatively small scene size has resulted in a “patchwork” archive of global imagery. The changes of solar geometries (BRDF) and phenology complicate land cover classification and change detection especially when comparing to the historical Landsat data archive. In this paper, we use the improved general empirical relation model (GERM) approach to normalize ASTER images acquired from different dates to one reference MODIS data. The resulting MODIS-like surface reflectance from different ASTER scenes can be mosaiced for land cover classification. Land cover change detection also becomes possible while comparing ASTER images to other mid-resolution data produced from same approach. * Corresponding author.