Gaps‐fill of SLC‐off Landsat ETM+ satellite image using a geostatistical approach

Abstract

Using appropriate techniques to fill the data gaps in SLC‐off ETM+ imagery may enable more scientific use of the data. The local linear histogram‐matching technique chosen by USGS has limitations if the scenes being combined exhibit high temporal variability and radical differences in target radiance due, for example, to the presence of clouds. This study proposes using an alternative interpolation method, the kriging geostatistical technique, for filling the data gaps. The case study shows that the ordinary kriging techniques may provide a powerful tool for interpolating the missing pixels in the SLC‐off ETM+ imagery. While the standardized ordinary cokriging has been shown to be particularly useful when samples of the variable to be predicted are sparse and samples of a second, related variable are plentiful, the case study demonstrates that it provides little improvement in interpolating the data gap in the SLC‐off imagery.

Acknowledgements

Support from the NASA Wisconsin Space Grant Consortium is appreciated. Constructive suggestions from two anonymous reviewers substantially improved the manuscript and are greatly appreciated.