Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments

Abstract

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 R 2 = 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.

Seasonal Progression of Ground Displacement Identified with Satellite Radar Interferometry and the Impact of Unusually Warm Conditions on Permafrost at the Yamal Peninsula in 2016

“…So far, Landsat with 30 m spatial resolution has been the main data source. Further relevant information can be also derived from C-band SAR data, specifically data acquired under frozen conditions [42][43][44]. Sentinel-1 and Sentinel-2 have been therefore combined.…”

“…In situ land-cover information was then applied to assign class names and define ROIs for the final maximum likelihood classification. This is based on a dedicated vegetation survey at VD (for details see [44]), the Yamal database [24], and a survey in the Northern Ural for land-cover types in the tundra taiga transition zone as a full transect was targeted. The classification accuracy ranges between 70 and 83.3% for the VD site with largest values for the dominant land-cover type 'dry to moist prostrate to erect dwarf shrub tundra' [28].…”

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

“…As temporal variations of backscatter can occur with changes in liquid water content, only winter data (December and/or January; frozen soil conditions) are used for cross-Arctic consistency and comparability (see Table 1). The use of such data also allows for simplified normalization with respect to the incidence angle influence on backscatter intensity when deriving the backscatter coefficient σ 0 , which is commonly used [53]. Acquisitions from July to early August (snow-free season) have been considered (see, e.g., the selected scenes of the validation granules in Table 2).…”

Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

“…In order to classify the images, a functional relationship between σ 0 and the incidence angle θ was calculated following a similar approach as suggested by Bartsch et al (2017) but applying a linear function as only a limited range is used (see also Widhalm et al, 2018;Bartsch et al, 2020; with a being the slope and b the intercept).…”