Akademik Veri Yönetim Sistemi
The first workshop of the Inter-Commission Committee on Geodesy for Climate Research (ICCC) of the International Association of Geodesy (IAG),“Geodesy for Climate Research”, 29 - 31 Mart 2021, ss.1
Monitoring of sea level variation is one of the most important parameters in climate change scenarios. Therefore, realizing and determining sea level variations both on a regional and global scale are of great importance. Conventionally, the sea level variations are determined using a tide gauge. However, the tide gauge records are also impressed by vertical land motions. The Global Navigation Satellite System (GNSS) is one of the major satellite technique and GNSS signals have some characteristics that can be used for remote sensing applications. GPS Interferometric Reflectometry (GPS-IR) technique allows to effectively determinate GPS-based sea level variations. In this context, this study aims to investigate the contribution to the GNSS stations located on the coasts of Turkey to the determination of sea level heights by using the GPS-IR method. For this purpose, first, utilized existing the Signal to noise ratio (SNR) data from TEKR GNSS station of the Turkish National Permanent Real Time Kinematic Network (TUSAGA-Active) and MERS GNSS station from the International GNSS Service (IGS) network. The GPS L1 signals are selected to retrieve sea level heights at these stations. The dominant multipath frequency of the SNR signal is computed with the Lomb-Scargle periodogram (LSP). Finally, the GPS-based sea level heights for these stations are compared to sea level records from nearby tide gauges. Secondly, the Singular Spectrum Analysis (SSA) method is applied to sea level heights derived from GPS-IR. SSA is a powerful filtration technique and split the noise from the signal. It is aimed to evaluate the effect of the SSA method on sea level heights derived from GPS-IR. The results are compared with the original time series of sea level variations. Consequently, it is demonstrated that the GPS-based sea level variations have improved correlation with SSA.