Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis

Abstract

Abstract-The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facil-ity of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite's cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistence time series of backscatter. Data quality control and management become very challenging tasks, when it comes to the large-scale data processing, both in terms of spatial coverage and data volume. In this paper, we evaluate three techniques for removing the Sentinel-1 border noise and compare the results with the existing "Sentinel-1 GRD Border Noise Removal" algorithm implemented in the Sentinel-1 toolbox of the Sentinel application platform.1 Validation and evaluation of the newly pro-posed algorithms was done using random samples containing 1500 Sentinel-1 scenes selected from a complete Sentinel-1 archive. The newly proposed approach has successfully achieved the required level of accuracy and solved the issue of time-series anomalies due to the border noise.Index Terms-C-band synthetic aperture radar (SAR), interfer-ometric wide swath (IW), Sentinel-1, Sentinel application platform (SNAP), Sentinel-1 border noise.quality insurance of large amounts of data at the scale of several hundreds of terabytes to petabyte have been becoming a challenging task.The recently launched constellation of two C-band synthetic aperture radar satellites named Sentinel-1 (A and B) is getting immense attention from both scientists and commercial users. Due to the open access data distribution policy and improved data quality in terms of temporal and spatial resolution, the Sentinel-1 user community is growing very fast. Daily global data acquisition of Sentinel-1 A and B is more than 1.5 TB [1]. In order to handle and process such big data for regional to global scale monitoring, reliable and stable processing chains are required.In this paper, we address the issue of image border noise in the Sentinel-1 GRDH (ground-range-detected high resolution) Level-1 product for large-scale data processing. The proposed methods are self-adaptive and are applicable to other acquisition modes, however, fine-tuning might be needed.

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

“…Another type of InSAR data failure described in the literature is a border noise [24][25][26][27]. This undesired processing artefact appeared in all of the Sentinel-1 GRD products generated before March 2018 [24].…”

“…This undesired processing artefact appeared in all of the Sentinel-1 GRD products generated before March 2018 [24]. Although this problem has been solved for the newly generated products, it did not cover the entire range of products and researchers still develop new methods and tools to effectively detect and remove this particular type of noise [24][25][26][27].…”

An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products

“…All the Level-1 GRD products generated by the IPF from its very first version, 2.34, to version 2.84 are known to be affected by undesired dark (non-zero) samples at the image borders [ 4 ]. More specifically, the products are affected by cross-track border noise in the range direction and along-track border noise in the azimuth direction [ 5 ]. Two examples are provided in Figure 1 a,b, where the border noise (left/right and top/bottom) has been highlighted in red.…”

“…The method’s validation was provided in the form of visual evaluation of generated arctic ice mosaic employing 450 Sentinel-1 scenes acquired in November 2017. Finally, in [ 5 ], Ali et al presented, in detail, the problem and proposed three different methods to address it for the case of IW GRDH products. The first one was an anomaly detection approach based on the interquantile range (IQR) Tukey test; the other two were bidirectional sampling methods (in slightly different formulations).…”

Sentinel-1 Data Border Noise Removal and Seamless Synthetic Aperture Radar Mosaic Generation

“…When the backscattering signals are low, e.g., from water, applying this tool can either leave lots of residuals or take off many valid data. A few other methods have been recently proposed to deal with these noise, and better results are shown comparing to those from the SNAP tool [6,7]. In order to appropriately remove border noises in scenes specifically over sea ice and open water, a method using line-by-line scanning and filtering is proposed in this paper.…”