Spatially heterogeneous land surface deformation data fusion method based on an enhanced spatio-temporal random effect mode

Authors: Shi, Qiang; Dai, Wujiao; Santerre, Rock; Li, Zhiwei
Abstract: The spatio-temporal random effect (STRE) model, a type of spatio-temporal Kalman filter model, can be used for the fusion of the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate high spatio-temporal resolution deformation series, assuming that the land deformation is spatially homogeneous in the monitoring area. However, when there are multiple deformation sources in the monitoring area, complex spatial heterogeneity will appear. To improve the fusion accuracy, we propose an enhanced STRE fusion method (eSTRE) by taking spatial heterogeneity into consideration. This new method integrates the spatial heterogeneity constraints in the STRE model by constructing extra-constrained spatial bases for the heterogeneous area. The effectiveness of this method is verified by using simulated data and real land surface deformation data. The results show that eSTRE can reduce the root mean square (RMS) of InSAR interpolation results by 14% and 23% on average for a simulation experiment and Los Angeles experiment, respectively, indicating that the new proposed method (eSTRE) is substantially better than the previous STRE fusion model
Document Type: Article de recherche
Issue Date: 7 May 2019
Open Access Date: 17 May 2019
Document version: VoR
Creative Commons Licence:
This document was published in: Remote Sensing, Vol. 11, ( 9), paper 1084, 2-19 (2019)
Alternative version: 10.3390/rs11091084
Collection:Articles publiés dans des revues avec comité de lecture

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