Recent studies have shown that processes like the thinning of the ice shelves, the acceleration of the outlet glaciers and the collapses of the ice shelves are connected to surface melting. Several ice shelves at the Antarctic Peninsula, a region that has experienced an atmospheric warming much larger than the global average, have retreated and disappeared. In order to assess the stability of the ice shelves, snowmelt amount can be used. Microwave remote sensing instruments are used in studying surface melting. The presence of liquid water causes a decrease in volumetric scattering that is dominant over absorption in dry snow, and increases absorption due to the increase of the imaginary part of snow permittivity. Thus, active microwave scatterometers can detect melt because of the decrease in backscatter. In this study, data from QuikSCAT (2000-2009) and ASCAT (2010-2016) are used for the investigation of the Antarctic surface melting dynamics for the period 2000-2016. The methodologies of Trusel et al. (2012) and Bothale et al. (2015) and two other threshold-based melt detection methodologies are applied and evaluated using in-situ meteorological records and are compared to passive microwave threshold-based melt duration results from AMSR-E and AMSR2. In addition, an attempt for a retrieval of a consistent melt time series is performed. The results support the use of a dynamic threshold-based melt detection approach for scatterometer data, but it is not possible to achieve consistency in melt duration results from the two scatterometers mainly due to their differences overpass timing. Overall, a high interannual variation in melt extent and melt index is found and results show that the extent of the areas that experience morning and afternoon melt consistently remains constant through the years. However, morning measurements underestimate melt and thus midday observations are important for melt detection studies, as melting varies throughout the day. In addition, although most of the large scale melting phenomena are captured by AMSR-E, the higher sensitivity of QuikSCAT to melt and its finer resolution result in differences in the derived melt metrics.