Abstract

The geometric accuracy of synthetic aperture radar (SAR) data is usually derived from level-1 products using accurately surveyed corner reflector positions. This paper introduces a novel approach that derives the range delay and azimuth shift from acquired SAR raw data (level-0 products). Therefore, the propagation path is completely retrieved from SAR pulse transmission up to the reception of the point target’s backscatter. The procedure includes simple pulse compression in range and azimuth instead of full SAR data processing. By applying this method, the geometric accuracy of ESA’s Sentinel-1 SAR satellites (Sentinel-1A and Sentinel-1B) is derived for each satellite overpass by using corner reflectors with precisely surveyed GPS positions. The results show that the azimuth bias of about 2 m found in level-1 products for Stripmap acquisitions is reduced to about 15 cm. This indicates an artificial bias arising from operational Sentinel-1 SAR data processing. The remaining range bias of about 1.0 m, observed in L0-products, is interpreted as the offset between the SAR antenna phase center and the spacecraft’s center of gravity. The relative pixel localization accuracy derived with the proposed method is about 12 cm for the evaluated acquisitions. Compared to the full processed level-1 SAR data products, this accuracy is similar in the range direction, but, for the azimuth direction, it is improved by about 50% with the proposed method.

Highlights

  • Synthetic aperture radar (SAR) images are commonly used for Earth observation

  • In order to address this issue, this paper proposes a new and applied method to verify the geometric accuracy based on SAR raw data (L0)

  • This paper proposes a novel and applied method to verify the geometric accuracy of SAR data products (L1)

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Summary

Introduction

Synthetic aperture radar (SAR) images are commonly used for Earth observation. The spatial resolution of SAR images has been improved for space-borne missions down to values in the order of meters and sub-meters in both the range and azimuth directions [1,2]. To ensure a geometric accuracy in the same order of magnitude, the geocoding step as part of the SAR data processor becomes a more challenging task, which includes a precise characterization of the measurement system and ancillary information like orbit accuracy, digital elevation models (DEMs) used as input, etc. For precise geolocation of SAR data products, the range-Doppler equations have to be solved using the annotated range and azimuth time as well as precise orbit data

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