Evaluation of single-band snow patch mapping using high resolution microwave remote sensing: an application to the Maritime Antarctic

Snow patch distribution and snow melt patterns during the summer are important controls for terrestrial ecosystems, permafrost and active layer, as well as for infrastructure access and management in the Maritime Antarctic. The mountainous terrain of the Maritime Antarctic and relatively small extent of the ice-free areas generate complex mosaics of numerous small snow-patches, ranging from tens to hundreds of meters in extension. These can only be accurately mapped using high resolution remote sensing sensors. However, the extremely high number of days with cloud cover limits the application of optical sensors from satellites, which have provided only sporadic snapshots in the Maritime Antarctic, limiting its use for monitoring purposes. In this paper we evaluate the application of Radar scenes from TerraSAR-X obtained in High Resolution SpotLight mode for mapping snow patches at a test area in Fildes Peninsula (King George Island, South Shetlands). Field analysis of the snow conditions, such as snow patch mapping and characterization of snow stratigraphy was conducted at the time of image acquisition in 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting. Ice-layers were frequent in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results in discrimination between wet-snow, lake water and bare soil. However, significant overlap in the backscattering signal was found. Average wet snow backscattering was −18.0 dB in HH mode, with water showing −21.1 dB and bare soil showing −11.9 dB. Single band pixel-based and object-oriented image classification methods were used to assess the classification potential of TerraSAR-X SpotLight imagery. The best results were obtained with an object-oriented approach using a watershed-based segmentation with a SVM classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to northwest facing snow patches, which showed significant error an issue probably related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in spotlight mode provides extremely high quality imagery for mapping wet snow and snow melt in the Maritime Antarctic. The classification procedure that we propose is a simple method and can easily be implemented in operational mode if a good digital elevation model is available.