Abstract
An updated algorithm for removal of thermal noise in Sentinel-1 synthetic aperture radar (SAR) Level-1 Ground Range Detected (GRD) data in cross-polarization is presented. The algorithm is comprised of two steps: correction of the annotated thermal noise magnitude (previously proposed in park2018) and a novel correction of the annotated thermal noise range dependence. The magnitude of the annotated thermal noise is corrected by applying scale and offset coefficients tuned on a few hundred of Sentinel-1 data acquired over surfaces with low backscatter in Interferometric Wide and Extra Wide swath modes in HV and VH polarizations. The values of coefficients for all modes and polarizations for data processed with Instrument Processing Facility (IPF) version 3.1 -3.3 are provided. The range dependence is corrected by minimizing a cost function between the annotated range profiles of thermal noise and antenna pattern gain (APG). An objective validation metric based on comparison of averaged backscatter at inter-swath boundaries is proposed. Validation is performed on hundreds of Sentinel-1 scenes acquired over open ocean, doldrums, deserts and sea ice. It shows that the new algorithm outperforms the standard thermal noise removal algorithm proposed by European Space Agency in almost all cases. Analysis shows that the new algorithm worsens noise correction in cases when the range dependence of the annotated APG does not match with the observed signal, indicating either problems with signal processing on IPF or imprecise annotation of APG.
Highlights
The paper is organized as follows: first we characterize the data used for training and validation, we present the algorithms for the thermal range noise shift / magnitude correction and the new validation metric, and we provide the coefficients that can be used for implementation of our algorithm of noise equivalent sigma zero (NESZ) modification and show and discuss the validation results
The individual coefficients kins are found under the assumption that after thermal noise correction and in case of low signal, the signal can be approximated with a linear dependence on incidence angle: σSN
Smaller patches result in noisier range quality metric (RQM) estimates, whereas larger patch sizes are less sensitive to the relative difference between sub-swaths
Summary
For validation of the thermal noise removal algorithm, independent Sentinel-1 data were used as specified in Table II. The regions which represent various surface types, and where the data have been collected, are mentioned in the list below: • Ocean: Norwegian Sea (65 – 75 N, -10 – 25 E).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
