Application of incoherent target decomposition theorems to classify snow cover over the Himalayan region

Abstract

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping.