@article{AbdullahiWesselHuberetal.2019,
  author    = {Abdullahi, Sahra and Wessel, Birgit and Huber, Martin and Wendleder, Anna and Roth, Achim and Kuenzer, Claudia},
  title     = {Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs11242903},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193902},
  year      = {2019},
  abstract  = {Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68\% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.},
  language  = {en}
}
@article{UllmannSchmittRothetal.2014,
  author    = {Ullmann, Tobias and Schmitt, Andreas and Roth, Achim and Duffe, Jason and Dech, Stefan and Hubberten, Hans-Wolfgang and Baumhauer, Roland},
  title     = {Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada},
  doi       = {10.3390/rs6098565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113303},
  year      = {2014},
  abstract  = {In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71\% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87\% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.},
  language  = {en}
}