Abstract:
Objectives The interferometric synthetic aperture radar (InSAR) technique has been widely applied to monitoring land deformation with very high spatiotemporal resolution. The elimination of atmospheric delay in the interferometric phase is critical to achieving higher accuracy of land deformation monitoring with the technique.
Methods This paper proposes a global navigation satellite system (GNSS) atmospheric delay modeling method based on the combination of the machine learning approach and the K-means algorithm. To evaluate the performance of the proposed, two Sentinel-1 short-time baseline interferograms in Southern California for summer 2019 and winter 2020 are used in the experiment.
Results The results after the correction of tropospheric delays show that the root mean square error of the interferometric phase is reduced by an average of 78%, which is significantly better than that of the conventional methods, including the power law function (highest 73%), generic atmospheric correction online service for InSAR estimation (61%), and weather models (58%).
Conclusions The experiment results indicate that the proposed method can effectively reduce the stratified tropospheric delay, and the modeled tropospheric delays are robust to surface deformation. Besides, the proposed method is potentially used in the areas with low spatial density GNSS stations in simulation experiments. This study can provide a reference for InSAR tropospheric delay spatiotemporal mapping based on GNSS atmospheric data.
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