Abstract
Estimates of snow cover area (SCA) can be obtained from spaceborne SAR sensors such as ERS, Radarsat and Envisat ASAR. A number of algorithms using backscattering images and interferometric coherence have been proposed and documented. It has also been shown that multifrequency and polarization SAR instruments gives enhanced snow mapping capabilities. In this paper we suggest improvements to the Nagler SCA-algorithm and apply them to Radarsat data in the Norwegian mountains. The effect of reduced backscattering due to increased incidence angle is discussed. The effect of mixed pixels (wet snow and other constituents) is addressed, and a sub-pixel method for snow classification is suggested. We also suggest classifying snow on lakes and in forests with different algorithms and separate masks, since the backscattering properties vary from class to class. Finally we try to assess the classification errors by statistical methods.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
