Blar i forfatter "Singha, Suman"

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    • Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements 

      Singha, Suman; Johansson, Malin; Hughes, Nick; Hvidegaard, Sine; Skourup, Henriette (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-04-26)
      In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne ...

    • High spatial and temporal resolution L- and C-band Synthetic Aperture Radar data analysis from the yearlong MOSAiC expedition 

      Johansson, Malin; Singha, Suman; Spreen, Gunnar; Howell, Stephen; Sobue, Shin-ichi; Davidson, Malcolm (Conference object; Konferansebidrag, 2021-04)
      In the yearlong MOSAIC expedition (2019-2020) R/V Polarstern drifted with sea ice through the Arctic Ocean, with the goal to continually monitor changes in the coupled ocean-ice-atmosphere system throughout the seasons. A substantial amount of synthetic aperture radar (SAR) satellite images overlapping the campaign was collected. Here, we investigate the change in polarimetric features over sea ice ...

    • Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band 

      Singha, Suman; Johansson, Malin; Doulgeris, Anthony Paul (Journal article; Tidsskriftartikkel, 2020-11-16)
      In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type ...

    • Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture 

      Guo, Wenkai; Itkin, Polona; Singha, Suman; Doulgeris, Anthony Paul; Johansson, Malin; Spreen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-16)
      We provide sea ice classification maps of a subweekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage ...

    • Towards operational sea ice type retrieval using L-band Synthetic aperture radar 

      Singha, Suman; Johansson, Malin; Doulgeris, Anthony Paul (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11-14)
      Operational ice services around the world have recognized the economic and environmental benefits that come from the increased capabilities and uses of space-borne Synthetic Aperture Radar (SAR) observation system. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large areal coverage, and on the other hand to obtain a radar response that carries as much ...

    • Year-around C- and L-band observation around the MOSAiC ice floe with high spatial and temporal resolution 

      Singha, Suman; Johansson, Malin; Spreen, Gunnar; Howell, Stephen; Shin-ichi, Sobue; Davidson, Malcolm (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-12)
      In September 2019, the German research icebreaker Polarstern started the largest multidisciplinary Arctic expedition, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to ice floes at high Arctic for a whole year, thus including the winter season, the main goal of the expedition is to better understand and quantify relevant processes ...