Modis data-based spatial consistency correction of low-resolution multi-source remote sensing imagery

The spatial consistency of low-resolution multi-source remote sensing data is of great importance for their combination in global change research. Currently, many methods are developed for the precise geometric correction of single kind of low-resolution data. However, the spatial consistency correction method is still need to be developed when many different kinds of low-resolution sensors' data are taken into consideration altogether, which is aimed to make their spectral data become consistent in geo-location. MODIS surface reflectance products, as they are of high accuracy of geo-location and data quality among low-resolution data, the spectral data of the multi-source low-resolution sensors are corrected to be consistency with it, which contain the level 1B data of NOAA/AVHRR, FY-3/VIRR, FY-3/MERSI, FY-2/VISSR. The proposed method in this paper can conducted spatial consistency correction on multi-source low-resolution remote sensing data precisely, efficiently, and automatically, which is based on contour point coarse matching and contour precise matching.