Geostatistical interpolation of missing data and downscaling of spatial resolution for remotely sensed atmospheric methane column concentrations

Since the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument on the Environmental Satellite (ENVISAT) was launched in 2002, CH₄ measurements from the satellite at regional or global scales became available. However, many gaps of missing data exist on the maps of the retrieved atmospheric CH₄ column concentrations from SCIAMACHY/ENVISAT. Moreover, the gridded CH₄ map with 50?×?50 km is a bit coarse for local interpretation. In this study, two geostatistical methods of ordinary kriging (OK) and ordinary cokriging (OCK) associated with 5 km normalized difference vegetation index (NDVI) images were examined to fill in missing data and to downscale the spatial resolution of CH₄ images. The 50 km CH₄ images interpolated by the two methods presented similar spatial patterns to the original 50 km CH₄ image and provided good results for the missing data. Taking into account the statistical results, the OCK method achieved better performance than OK in filling gaps of missing data. In further downscaling the CH₄ image from 50 to 5 km, the OCK method achieved a significant amount of spatial detail, and the statistical results also showed that OCK performed better than OK.