Wide-area coastal deformation extraction using multi-path/frame InSAR: A case study of the Bohai Rim

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-09-02 DOI:10.1016/j.rse.2025.114988

Zhiqiang Gong , Mingsheng Liao , Jie Dong , Qianye Lan , Ru Wang , Shangjing Lai

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引用次数: 0

Abstract

Coastal areas worldwide experience serious ground subsidence and rising sea levels, leading to frequent flooding and continued elevation loss. Reliable estimation of coastal subsidence is critical for effective risk assessment and management. Interferometric Synthetic Aperture Radar (InSAR) enables millimeter-scale deformation monitoring, but generating seamless wide-area results remains challenging due to limited swath width and inconsistencies between adjacent frames. This study proposes an adaptive gridded adjustment model for merging multi-path/frame InSAR results into a wide-area high-precision deformation map. We employ a quadtree-based adaptive grid with dynamically optimized sizes determined by the deformation gradient. The gridded corrections are combined with GNSS-constrained global corrections to eliminate inter-frame biases. Applied to the coastal area of the Bohai Rim, this method reduces the Sentinel-1 discrepancies by 38 %, and outperforms the fixed-grid method in achieving an optimal balance between precision and efficiency. This method is useful for wide-area high-precision deformation monitoring to support risk assessments of relative sea level rise in the context of global climate change.

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基于多路径/帧InSAR的广域海岸形变提取——以环渤海地区为例

世界各地的沿海地区都经历着严重的地面沉降和海平面上升，导致频繁的洪水和持续的海拔下降。沿海沉降的可靠估计对有效的风险评估和管理至关重要。干涉合成孔径雷达（InSAR）可以实现毫米尺度的变形监测，但由于条带宽度有限和相邻帧之间的不一致性，产生无缝的广域结果仍然具有挑战性。本文提出了一种自适应网格平差模型，用于将多路径/帧InSAR结果合并成广域高精度形变图。我们采用基于四叉树的自适应网格，根据变形梯度动态优化大小。网格校正与gnss约束的全局校正相结合，以消除帧间偏差。应用于环渤海沿海地区，该方法将Sentinel-1的误差降低了38%，在精度和效率之间取得了最佳平衡，优于固定网格方法。该方法可用于全球气候变化背景下的广域高精度形变监测，以支持相对海平面上升的风险评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.