Optimizing GNSS-IR for sea level monitoring: a case study along the Eastern Black Sea coast of Türkiye
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Cansu Beşel Hatipoğlu
Abstract
Determining the optimal parameter conditions based on satellite elevation angle and azimuth ranges allows the examination of the reflection surface at the GNSS site locations. This study aims to assess whether the GNSS site locations along the Trabzon coast in the Eastern Black Sea region of Türkiye meet the optimal parameter conditions for monitoring of sea level changes using GNSS-IR. Additionally, the impact of different topographical features and sea conditions was assessed for parameter selection. For this purpose, an experimental area was selected along the coast, and GNSS receivers were installed at four experimental points (NOK1, NOK2, NOK3, NOK4) with different topographic characteristics. A 3-day measurement campaign was conducted to determine the optimal parameter conditions. Signal-to-noise ratio (SNR) data from these four experimental points were used in the study. Satisfactory results could not be obtained at NOK1 and NOK2. Only the NOK4 could be compared with the nearby tide gauge. A relatively high root mean square error of 20 cm was obtained between the two datasets. The results indicate that the data collected from the experimental points were affected by the optimal parameter conditions. Moreover, the GNSS-IR-derived sea level was influenced by the environmental circumstances in locations with optimum parameters.
Funding source: Scientific and Technological Research Council of Turkey
Award Identifier / Grant number: 1002 - A Short term Support Module
Acknowledgments
The author acknowledges the TÜBİTAK 123Y469 with project number Scientific and Technological Research Council of Turkey. Google Earth provided the satellite image of Figures 7–10. ChatGPT was used for English language checks in certain sections.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: C.B.H.: Conceptualization, Investigation, Methodology, Analysis, Writing.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: The Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the 1002 - A Short term Support Module.
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Data availability: The data that has been used if confidential.
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