A Visible Spectral Atlas of Geostationary Satellites

The Robotic Automated Pointing Telescope for Optical Reflectance Spectroscopy (RAPTORS) is an automated 0.6meter, f/4.6 telescope constructed by five engineering students at the University of Arizona. The telescope is fitted with a transmission grating with a spectral resolution of R~30 in the visible wavelength range (0.45 – 0.95 microns), resulting in a roughly 15 nm resolution. This system has allowed us to conduct the initial phase of a slitless spectroscopic survey for over 60 GEO-belt satellites. Observations were conducted with a cadence of at least one spectrum per minute, producing an average of ~800 spectra per night. Data calibration included flats once per lunation, CCD darkand bias-frames each night, and nightly spectra of a G2V solar analog star for solar reflectance calibration. Although empirical results have shown that a single solar analog is sufficient for visible reflectance measurements, additional airmass correction stars were also observed most nights to better account for atmospheric extinction effects if needed. Initial results from the survey show that visible spectroscopy is a powerful tool for satellite characterization and discrimination. We present examples of an individual satellite’s 3-dimensional Spectral Phase Map (SPM) of longitudinal phase angle vs. wavelength vs. normalized reflectance (out-of-plane axis). In addition to the spectral data, the zeroth order of the images can be used to produce uncalibrated, panchromatic lightcurves for more traditional satellite characterization methods. Comparisons between the SPM and zeroth order lightcurves show good correlation between traditional glint features and the features in phase-wavelength space. Additional “flux-neutral” color features are also identifiable which maintain the satellite’s brightness in the lightcurve but change the reflectance at certain wavelengths. Complementary to the phase-wavelength maps, full-night median spectra are presented as a tool for target characterization and discrimination. Initial results show a strong correlation between satellite bus-type and the median spectrum’s shape across multiple satellites. This is promising for the prospect of creating a bus-type based GEO spectral taxonomy. These results show that the GEO spectral survey meets its goals of providing methods to discriminate targets via lightcurve data and Spectral Phase Maps as well as for fingerprinting individual satellites. It is possible that these spectral data represent the foundations for a GEO satellite taxonomy, which exceeds the expectations of this survey. Due to the promising nature of the initial results of this survey, ongoing observational efforts are focusing on a subset of targets that will be representative of their bus type. Four bus types were chosen for an in-depth analysis with three satellites in each bus type. Satellites were chosen such that constituents of a given group are separated by no more than 15 degrees in orbital longitude to limit viewing geometry effects on the study. Each of the selected targets will be observed once per month over a year to investigate seasonal variations in the full-night median spectra. Target observations will be clustered by bus type to reduce night-to-night seasonal variations within the bus group. Potential future work could include machine learning techniques on SPMs for object discrimination and bustype identification, spectral modeling for material composition extraction, increased spectral coverage, or expansion of the survey to other longitudes in the U.S.