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

• Published in: The Cryosphere
• Main Authors: Mora, Carla, Jiménez, Juan Javier, Pina, Pedro, Catalão, João, Vieira, Gonçalo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2017
• Subjects: article; Verlagsveröffentlichung; Antarctic; King George Island; Fildes; Fildes peninsula; Antarc*; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-11-139-2017; https://noa.gwlb.de/receive/cop_mods_00010530; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00010487/tc-11-139-2017.pdf; https://tc.copernicus.org/articles/11/139/2017/tc-11-139-2017.pdf

The mountainous and ice-free terrains of the maritime Antarctic generate complex mosaics of snow patches, ranging from tens to hundreds of metres. These can only be accurately mapped using high-resolution remote sensing. In this paper we evaluate the application of radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping snow patches at a test area on Fildes Peninsula (King George Island, South Shetlands). Snow-patch mapping and characterization of snow stratigraphy were conducted at the time of image acquisition on 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting and with frequent ice layers in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results when discriminating 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 segmentation with a support vector machine (SVM) classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to north-west facing snow patches, which showed significant error, an issue related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in SpotLight mode provides high-quality imagery for mapping wet snow and snowmelt in the maritime Antarctic. The classification procedure that we propose is a simple method and a first step to an implementation in operational mode if a good digital elevation model is available.