Abstract

One of the major advantages of the Sentinel-1 data is its capability to provide very high spatio-temporal coverage allowing the mapping of large areas as well as creation of dense time-series of the Sentinel-1 acquisitions. The SGRT software developed at TU Wien aims at automated processing of Sentinel-1 data for global and regional products. The first step of the processing consists of the Sentinel-1 data geocoding with the help of S1TBX software and their resampling to a common grid. These resampled images serve as an input for the product derivation. Thus, it is very important to select the most reliable processing settings and assess the geocoding uncertainty for both backscatter and projected local incidence angle images. Within this study, selection of Sentinel-1 acquisitions over 3 test areas in Europe were processed manually in the S1TBX software, testing multiple software versions, processing settings and digital elevation models (DEM) and the accuracy of the resulting geocoded images were assessed. Secondly, all available Sentinel-1 data over the areas were processed using selected settings and detailed quality check was performed. Overall, strong influence of the used DEM on the geocoding quality was confirmed with differences up to 80 meters in areas with higher terrain variations. In flat areas, the geocoding accuracy of backscatter images was overall good, with observed shifts between 0 and 30m. Larger systematic shifts were identified in case of projected local incidence angle images. These results encourage the automated processing of large volumes of Sentinel-1 data.

Keywords

Sentinel-1; S1TBX; geocoding uncertainty

Published in

Proceedings of SPIE
2016, Volume: 10004
ISBN: 978-1-5106-0413-1, eISBN: 978-1-5106-0412-4
Publisher: SPIE-INT SOC OPTICAL ENGINEERING, PO BOX 10, BELLINGHAM, WA 98227-0010 USA

Conference

Conference on Image and Signal Processing for Remote Sensing XXII, SEP 26-28, 2016, Edinburgh, SCOTLAND

SLU Authors

• Persson, Henrik

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing


Publication identifier

DOI: https://doi.org/10.1117/12.2240840

Permanent link to this page (URI)

https://res.slu.se/id/publ/117848