High-resolution sub-canopy topography mapping via TanDEM-X DEM combined with future P-band BIOMASS PolInSAR data

Zhu, Jianjun; Liu, Zhiwei; Fu, Haiqiang; Zhou, Cui; Zhou, Yi; Wang, Huiqiang; Xie, Yanzhou

doi:10.1007/s00190-023-01807-0

Cited by 1 publication

(1 citation statement)

References 69 publications

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“…As two current advanced deformation monitoring technologies, interferometric synthetic aperture radar (InSAR) and Global Navigation Satellite System (GNSS) are the main sources of high-precision deformation monitoring data (Takada et al 2018;Xu et al 2018;Yan et al 2022b;Zhang et al 2023;Zhu et al 2023). GNSS sites are mostly deployed with deep anchors in bedrock or anchored through reinforced concrete structures and stable buildings (Xi et al 2021;Xu et al 2022); thus, these monitoring sites have fixed location properties.…”

Section: Introductionmentioning

confidence: 99%

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Yan,

Dai,

et al. 2024

Earth Planets Space

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Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios. Graphical Abstract

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