Improving the Geolocation Algorithm for Sensors Onboard the ISS: Effect of Drift Angle

Abstract

Abstract:The drift angle caused by the Earth's self-rotation may introduce rotational displacement artifact on the geolocation results of imagery acquired by an Earth observing sensor onboard the International Space Station (ISS). If uncorrected, it would cause a gradual degradation of positional accuracy from the center towards the edges of an image. One correction method to account for the drift angle effect was developed. The drift angle was calculated from the ISS state vectors and positional information of the ground nadir point of the imagery. Tests with images acquired by the International Space Station Agriculture Camera (ISSAC) using Google Earth TM as a reference indicated that applying the drift angle correction can reduce the residual geolocation error for the corner points of the ISSAC images from over 1000 to less than 500 m. The improved geolocation accuracy is well within the inherent geolocation uncertainty of up to 800 m, mainly due to imprecise knowledge of the ISS attitude and state parameters required to perform the geolocation algorithm.

Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI

“…In intercalibration between hyperspectral sensors, sources of uncertainty include radiometric and spectral calibration uncertainties, the effects of differences in the spatial resolution, relative geolocation errors, and polarization sensitivity differences, among others. Specifically, the geometric accuracy of the products of the sensors on the ISS may be low compared with the accuracies of satellites flying at around 700 km, because the ISS experiences large variations in attitude and altitude [57]. The magnitudes of these effects on intercalibration should be investigated in a separate study.…”

Earth observation from the manned low Earth orbit platforms

“…Additionally, changes in the center of mass caused by addition of modules and installation/removal of instruments, and frequent, though slow and steady, variations in both attitude and altitude of the ISS pose great challenges for georeferencing of the collected data. Attempting to mitigate this limimition, Dou et al (2013Dou et al ( , 2014 developed an algorithm for georeferencing the ISSAC imagery acquired onboard the ISS, which can be easily modified to suit other ISS sensors as well.…”

“…The ISSAC payload system was launched via the Japanese H-II Transfer Vehicle (HTV-2) to the ISS in January 2011 and was in operation from March 2011 to March 2013. The ISSAC optical system was radiometrically calibrated (Olsen et al, 2010), and a georeferencing algorithm was developed to compute geographic locations of its imagery (Dou et al, 2014(Dou et al, , 2013. The imagery acquired by ISSAC was available to the public free of charge through the Digital Northern Great Plains to help the farmers and ranchers in this region to practice precision agriculture.…”

Improving GEDI Footprint Geolocation using a High Resolution Digital Terrain Model

“…GEDI footprint geolocations are derived from GEDI's own Inertial Measurement Unit (IMU), Global Positioning Systems (GPS) and star tracker sensors onboard the ISS [2], [4], [5]. However, the ISS's low orbit, size and shape result in increased mechanical vibrations and greater variations in orientation and altitude than traditional Earth Observation satellites [6]. Consequently, the horizontal position precision of GEDI footprints was expected at 10 m after calibration [2].…”