Abstract
Artificial radar reflectors, such as corner reflectors or transponders, are commonly used for radiometric and geometric Synthetic Aperture Radar (SAR) sensor calibration, SAR interferometry (InSAR) applications over areas with few natural coherent scatterers, and InSAR datum connection and geodetic integration. Despite the current abundance of regular SAR time series, no free and open-source software (FOSS) dedicated to analyzing SAR time series of artificial radar reflectors exists. In this paper, we present a FOSS Python toolbox for efficient and automatic estimation of: (i) the clutter level of a particular site before a corner reflector installation, (ii) the Radar Cross Section (RCS) to track a corner reflector’s performance and detect outliers, for example, due to damage or debris accumulation, (iii) the Signal-to-Clutter Ratio (SCR) to predict the positioning precision and the InSAR phase variance, (iv) the InSAR displacement time series of a corner reflector network. We use the toolbox to analyze Sentinel-1 SAR time series of the network of 23 corner reflectors for InSAR monitoring of landslides in Slovakia.
Highlights
We summarize the theoretical particulars of the Synthetic Aperture Radar (SAR) measurements of artificial radar reflectors from radiometric, geometric and interferometric perspectives
A SAR measurement is represented by the impulse response function (IRF)
To verify the stability of the reference corner reflectors (CR) (LHE-4), we present the results of the datum-free network solution
Summary
SAR observations are positioned in a dimensionless 2D radar datum (azimuth and range). Via subsequent transformations, utilizing the range-Doppler equations and an external elevation model [14,15], these positions can be transformed to a Cartesian geocentric terrestrial reference frame (TRF), for example, the ITRF2014 realization of the International. Artificial radar reflectors, approximating ideal radar point scatterers (PS) with precisely known positions of their effective phase centres, are utilized to quantify these positioning errors. They can improve the geolocation accuracy of a nearby natural PS [17,18]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
