  We present novel techniques and methodology for unresolved photometric
characterization of low-Earth Orbit (LEO) satellites. With the Pomenis LEO
Satellite Photometric Survey our team has made over 14,000 observations of
Starlink and OneWeb satellites to measure their apparent brightness. From the
apparent brightness of each satellite, we calculate a new metric: the effective
albedo, which quantifies the specularity of the reflecting satellite. Unlike
stellar magnitude units, the effective albedo accounts for apparent range and
phase angle and enables direct comparison of different satellites. Mapping the
effective albedo from multiple observations across the sky produces an all-sky
photometric signature which is distinct for each population of satellites,
including the various sub-models of Starlink satellites. Space Situational
Awareness (SSA) practitioners can use all-sky photometric signatures to
differentiate populations of satellites, compare their reflection
characteristics, identify unknown satellites, and find anomalous members. To
test the efficacy of all-sky signatures for satellite identification, we
applied a machine learning classifier algorithm which correctly identified the
majority of satellites based solely on the effective albedo metric and with as
few as one observation per individual satellite. Our new method of LEO
satellite photometric characterization requires no prior knowledge of the
satellite's properties and is readily scalable to large numbers of satellites
such as those expected with developing communications mega-constellations.
