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Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an effective technique for monitoring land subsidence due to its high spatial resolution and capability to measure ground displacement over large areas. However, validating PS-InSAR measurements with Global Navigation Satellite System (GNSS) data is essential to ensure accuracy. This study examines the correlation between PS-InSAR and GNSS measurements by analyzing the proximity of 255 GNSS points to 14,800 PS-InSAR records. To avoid high error rates in assessing land subsidence, PS-InSAR records were validated against 255 GNSS points using the following method: (1) employing a near tool with a 100 m distance criterion from the GNSS points and a vertical deviation threshold of −1 to 1 cm between the GNSS points and the nearest PS-InSAR records within 100 m; (2) applying the Nash-Sutcliffe Efficiency (NSE) matrix to compare the residual variance of the model (PS-InSAR) to the variance of the measured data (GNSS). The data treatment procedures resulted in 21 matching data points from both GNSS and PS-InSAR datasets, with a high NSE value of 0.746, indicating a strong correlation. This approach demonstrates that careful data selection based on proximity and refinement using statistical measures can significantly enhance the reliability of PS-InSAR data for more detailed analysis.

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Enhancing the Accuracy of Land Subsidence Monitoring: Correlation and Validation of PS-InSAR with GNSS Data Using the Nash-Sutcliffe Efficiency Matrix

  • 会议论文

  • 发表日期：

    2025年

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  • Edy Trihatmoko,
  • Joko Widodo,
  • Nurlinda,
  • Shinichi Sobue

摘要

Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an effective technique for monitoring land subsidence due to its high spatial resolution and capability to measure ground displacement over large areas. However, validating PS-InSAR measurements with Global Navigation Satellite System (GNSS) data is essential to ensure accuracy. This study examines the correlation between PS-InSAR and GNSS measurements by analyzing the proximity of 255 GNSS points to 14,800 PS-InSAR records. To avoid high error rates in assessing land subsidence, PS-InSAR records were validated against 255 GNSS points using the following method: (1) employing a near tool with a 100 m distance criterion from the GNSS points and a vertical deviation threshold of −1 to 1 cm between the GNSS points and the nearest PS-InSAR records within 100 m; (2) applying the Nash-Sutcliffe Efficiency (NSE) matrix to compare the residual variance of the model (PS-InSAR) to the variance of the measured data (GNSS). The data treatment procedures resulted in 21 matching data points from both GNSS and PS-InSAR datasets, with a high NSE value of 0.746, indicating a strong correlation. This approach demonstrates that careful data selection based on proximity and refinement using statistical measures can significantly enhance the reliability of PS-InSAR data for more detailed analysis.