This document discusses two Kalman filtering approaches to automate the correction of long-term geolocation errors in MODIS and VIIRS instruments. The current manual approach uses least-squares analysis with linear and sinusoidal curves, but the Kalman filtering approaches can provide automated daily updates. The first approach models error trends with linear and sinusoidal curves in a Kalman filter. The second estimates sensor roll, pitch and yaw errors through Euler angle differential equations. Preliminary tests of the first approach on over 10 years of MODIS data show promising results in reducing geolocation errors, while the second approach requires more examination. Further testing is needed to validate and refine the Kalman filtering methods for automated long-term geolocation error correction.

1. MODIS and VIIRS Geolocation Error andLong-term Trend Analysis with Automated Correction Techniques using KalmanFiltering Masahiro Nishihama Sigma Space Corporation Lanham, Maryland, USA (20706) mash.nishihama@nasa.gov Robert E. Wolfe NASA Goddard Space Flight Center, Code 614.5 Greenbelt, Maryland, USA (20771) robert.e.wolfe@nasa.gov IGARSS 2011 July 24-29, Vancouver Canada

3. Initial on-orbit bias removal is performed immediately after launch

4. Analysis and removal of long-termgeolocation bias is needed to maintain accurate geolocation throughout multi-year missions

5. Analysis and removal of the within-orbit trend is also needed because of thermal effects on instrument pointing

6. Current approach is a manual least-squares analysis that uses linear and sinusoidal curves to remove both the long-term and within-orbit trends

7. This presentation discusses twoKalmanfiltering approaches that are being considered to replace the current manual least-squares approach

8. These new techniques can be applied to the Moderate-Resolution Imaging Spectroradiometer (MODIS) instruments and the future Visible Infrared Imaging Radiometer Suite (VIIRS) instrument.IGARSS’11-Nishihama et al. 2

19. IGARSS’11-Nishihama et al. 13

27. DISCUSSION Analysis of both Kalman filtering methods are preliminary, but results of the first method is very promising. The second method needs more tests and examination. Appearance of yaw in the second method is interesting and needs investigation. Further tests will be needed for Aqua data using both methods. Determination of the initial covariance matrices and other parameters is critical, and requires careful attention. The Kalman filters methods are computationally efficient, it took very little time to processing 3800 sample points (~250 ground points are acquired each day). Once fully tested, either of the methods could be implemented, enabling automated daily geolocation parameter updates. IGARSS’11-Nishihama et al. 21