The incidence angle is a crucial parameter for synthetic aperture radar (SAR). The local incidence angle differs greatly in mountainous areas, and it may lead to quite different backscatter properties for the same ground targets for different incidence angles. As a result, it has to be taken into consideration in image classification. This work demonstrates the importance of the local incidence angle and the necessity of considering this angle in SAR image classification in mountainous areas. A local incidence angle referenced method based on support vector machines is developed to perform classification. In this method, the datasets are divided into different zones according to local incidence angle, and the training and predicting are performed within these zones to eliminate the influence of the local incidence angles. The experiments are performed around the Dongkemadi Glacier in the central Qinghai-Tibetan plateau using two RADARSAT-2 polarimetric SAR images. This paper concentrates on the effect of the local incidence angles in SAR image classification. Compared to the method using only backscatter coefficients, the proposed method improves the overall classification accuracy by 6 to 8%. More important, it is verified that this method is more helpful for the ground cover types where the terrain changes sharply.
The snow water equivalent (SWE) products from passive microwave remote sensing are useful in global climate change studies due to the long-time and all-weather imaging capabilities of passive microwave radiometry at the hemisphere scale. Northern Hemisphere SWE products, including products from the National Snow and Ice Data Center (NSIDC) and GlobSnow from the European Space Agency (ESA), have been providing long-time series information since 1979. However, the different algorithms used to produce the NSIDC and GlobSnow products lead to discrepancies in the data. To determine which product might be superior, this paper assesses their hemisphere-scale quality for the time period 1979−2010. By comparing the data with historical snow depth measurements obtained from 7388 meteorological stations in the Northern Hemisphere, the accuracies of the different SWE products are analyzed for the period and for different snow types. The results show that for SWEs above 30 mm but below 200 mm, GlobSnow estimates maintain a better linear relation with the ground measurements. NSIDC products are more influenced by microwave “saturation,” producing obvious underestimations for SWEs over 120 mm. However, for shallow snow (SWE less than 30 mm), the slight overestimate produced by GlobSnow is more obvious than that of the other NSIDC products.