Normalized C-HH backscatter from Sentinel-1 (December, 2014-2017) for selected tundra regions

• Main Authors: Widhalm, Barbara, Bartsch, Annett, Goler, Robert
• Format: Dataset
• Language: English
• Published: PANGAEA 2019
• Subjects: Alaska; USA; AWI_PerDyn; C01_Barrow; C02_Toolik; C03_Teshekpuk; C04_Mackenzie_Delta; C07_Lena_Delta; C14_UsaBasin; Cryosphere; ESA Data User Element - GlobPermafrost; ESA-DUE-GlobPermafrost; Event label; File content; File format; File name; File size; MON; Monitoring; Permafrost Research (Periglacial Dynamics) @ AWI; Siberia; Russia; T01_Westsiberia; Uniform resource locator/link to file; Usa River basin; Northeast European Russia; Canada; Mackenzie Delta; Yamal Peninsula; Barrow; lena delta; permafrost; Tundra
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.897046; https://doi.org/10.1594/PANGAEA.897046

Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas. The following regions are covered in the dataset: Yamal peninsula (Russia), Usa Basin (Russia), Lena Delta (Russia), Mackenzie Delta (Canada), Barrow, Toolik and Teshekpuk Lake region (Alaska).