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Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

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Summary
This summary is machine-generated.

This study introduces an optimized Interferometric Synthetic Aperture Radar (InSAR) baseline method to improve monitoring accuracy by accounting for vegetation changes. The new approach significantly reduces noise and enhances surface deformation detection in the Yuanmou dry-hot valley.

Keywords:
InSARYuanmou dry-hot valleybaseline optimizationvegetation coverage

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Area of Science:

  • Geodesy
  • Earth Observation
  • Remote Sensing

Background:

  • Time-series Interferometric Synthetic Aperture Radar (InSAR) is vital for Earth observation but accuracy is limited by baseline network quality.
  • Surface vegetation changes introduce incoherent noise, degrading interferogram quality and InSAR monitoring accuracy.
  • Optimizing the interferometric baseline is crucial for enhancing InSAR's precision.

Purpose of the Study:

  • To propose and validate an optimization method for the InSAR baseline that specifically addresses vegetation coverage changes (OM-InSAR-BCCVC).
  • To improve the accuracy of surface deformation monitoring in the Yuanmou dry-hot valley by mitigating vegetation-induced noise.

Main Methods:

  • Categorized SAR image pairs based on vegetation coverage (high/low) using imaging times.
  • Removed image pairs with coherence coefficients below category averages.
  • Applied Small Baseline Subset InSAR (SBAS-InSAR) for surface deformation retrieval and optical remote sensing for landslide verification.

Main Results:

  • Seasonal vegetation changes in the Yuanmou dry-hot valley cause significant InSAR coherence variations, with lowest coherence from July-September.
  • The OM-InSAR-BCCVC method improved the interferometric map ratio by 17.5% and reduced inversion error RMSE by 0.5 rad compared to unoptimized methods.
  • Identified fifteen landslides and potential sites, with maximum subsidence exceeding 100 mm, using optimized InSAR data from Jan 2021 to May 2023.

Conclusions:

  • The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation changes, significantly improving InSAR monitoring accuracy.
  • Optimized InSAR baseline networks are essential for reliable surface deformation monitoring in vegetated, dynamic environments.
  • The study demonstrates the practical application of OM-InSAR-BCCVC for landslide identification and monitoring in the Yuanmou dry-hot valley.