MODIS and VIIRS geolocation error and long-term trend analysis with automated correction techniques using Kalman filtering

Accurate geolocation of remote sensing data is vital to all aspects of Earth science research and applications, especially over heterogeneous land surfaces. To achieve and maintain accurate geolocation throughout multi-year missions, an initial on-orbit bias removal is followed by analysis and removal of the long term trends in the instrument pointing caused by temporal changes in the instrument and spacecraft materials and thermal environment. This research is focused on automating this long-term trend analysis and removal by using a Kalman filtering approach. We will demonstrate an application of this technique using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instruments and show how it could be used for the future Visible Infrared Imaging Radiometer Suite (VIIRS) instrument.