Over half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.