EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

SarXarray: an Xarray extension for SLC SAR data processing 

Ou Ku1, Francesco Nattino1, Meiert Grootes1, Pranav Chandramouli1, and Freek van Leijen2
Ou Ku et al.
  • 1Netherlands eScience Center, Science Park 140, 1098 XG Amsterdam, the Netherlands
  • 2Department of Geoscience & Remote Sensing, Delft University of Technology (TU Delft), Delft, the Netherlands

Satellite-based Interferometric Synthetic Aperture Radar (InSAR) plays a significant role for numerous surface motion monitoring applications, e.g. civil-infrastructure stability, hydrocarbons extraction, etc. InSAR monitoring is based on a coregistered stack of Single Look Complex (SLC) SAR images. Due to the long temporal coverage, broad spatial coverage and high spatio-temporal resolution of an SLC SAR stack, handling it in an efficient way is a common challenge within the community. Aiming to meet this need, we present SarXarray: an open-source Xarray extension for SLC SAR stack processing. SarXarray provides a Python interface to read and write a coregistered stack of SLC SAR data, with basic SAR processing functions. It utilizes Xarray’s support on labeled multi-dimensional datasets to stress the space-time character of an SLC SAR stack. It also leverages Dask to perform lazy evaluation of the operations. SarXarray can be integrated to existing Python workflows in a flexible way. We provide a case study of creating a SAR Mean Reflectivity Map to demonstrate the functionality of SarXarray.

How to cite: Ku, O., Nattino, F., Grootes, M., Chandramouli, P., and van Leijen, F.: SarXarray: an Xarray extension for SLC SAR data processing , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6768,, 2023.