Spatiotemporal Ocean Tidal Loading in InSAR Measurements Determined by Kinematic PPP Solutions of a Regional GPS Network
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Online Publication Date2020-06-16
Print Publication Date2020
Permanent link to this recordhttp://hdl.handle.net/10754/664451
MetadataShow full item record
AbstractThe coastal crustal deformation caused by ocean tidal loading (OTL) varies spatially and temporally, and this spatiotemporal variation in satellite-based interferometric synthetic aperture radar (InSAR) measurements needs to be determined. In this article, we propose a spatiotemporal modeling method to estimate the OTL displacements in InSAR measurements using the kinematic precise point positioning (PPP) solutions of a regional GPS network. We tested the method through an experiment using 25 Sentinel-1B images and long-term observations of 172 GPS reference sites from Southern California. The experimental results suggest that there are significant OTL and solid Earth tide effects in the differential InSAR interferogram, which is greater than 40 mm. We find that the spatial characteristics of OTL variations can be expressed as a high-order polynomial in the two variables of latitude and longitude, and the spatiotemporally modeled PPP tidal estimates of the high-density GPS sites can provide high precision OTL correction for all the pixels in the interferogram. In the last part of the study, we show that the spatial large-scale signals in the differential interferograms of Sentinel-1B data are mainly atmospheric delay, solid Earth tidal, and OTL effect, and demonstrate the importance of the tidal correction in the InSAR measurements.
CitationPeng, W., Wang, Q., Zhan, F. B., & Cao, Y. (2020). Spatiotemporal Ocean Tidal Loading in InSAR Measurements Determined by Kinematic PPP Solutions of a Regional GPS Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3772–3779. doi:10.1109/jstars.2020.3002777
SponsorsThis work was supported in part by the National Natural Science Foundation of China under Grant U1531128 and Grant 41820104005, and in part by the Hunan Provincial Innovation Foundation for Postgraduate under Grant CX2017B053. The work of Wei Peng was supported by the China Scholarship Council for a 1-year study abroad.
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution 4.0 License.