A functional model for determining maximum detectable deformation gradients of InSAR considering the topography in mountainous areas
The maximum detectable deformation gradients (MDDG) for interferometric synthetic aperture radar (InSAR) technology is important for the selection of SAR images and processing algorithms to perform accurate slope displacement monitoring, which is strongly influenced by terrain factors in mountainous areas. In this paper, a functional model is proposed to determine the MDDG of InSAR with respect to arbitrary slope gradients/aspects and wavelengths. Based on this model, regional MDDG characteristics are explored and compared in Mao County, Sichuan Province, China. The MDDG distribution regarding on Sentinel-1, ALOS-2/PALSAR-2 and TerraSAR-X SAR satellite data using arbitrary slope gradient/aspect are derived. Furthermore, the MDDG from variable satellites for three different bands (X/C/L-band) are compared and the influence factors with respect to the wavelength and resolution on MDDG are discussed. The proposed model is helpful in selecting of SAR data or processing algorithms based on calculated MDDG, in the meanwhile, it has significant implications on the understanding and analyzing real slope displacement monitored by InSAR regarding on different SAR images in mountainous areas.
Funding
National Natural Science Foundation of China Major Program (41941019)
China Postdoctoral Science Foundation (2020M673322)
National Key Research and Development Program of China (2021YFB3901403)
Sichuan Province Science Fund for Distinguished Young Scholars (2023NSFSC1909)
Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area: (20220006)
State Key Laboratory of Geohazard Prevention and Geoenviroment Protection Independent Research Project (SKLGP2020Z012)
History
School
- Architecture, Building and Civil Engineering
Published in
IEEE Transactions on Geoscience and Remote SensingVolume
61Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2023-06-08Publication date
2023-06-19Copyright date
2023ISSN
0196-2892eISSN
1558-0644Publisher version
Language
- en