author：Nazi Wang ， Tianhe Xu , Fan Gao , Yunqiao He , Xinyue Meng, Lili Jing, and Baojiao Ning
来源出版物：IEEE Transactions on Geoscience and Remote Sensing(Volume: 60) 文献号：DOI:10.1109/TGRS.2022.3219074出版年：Nov 2022
abstract：Compared with tide gauges, global navigation satellite system multipath reflectometry (GNSS-MR) can provide lowcost, long-term sea-level data that are not susceptible to crustal loading. Signal-to-noise ratio (SNR) observables in GNSS files are commonly used for GNSS-MR; however, these observables are not always present, especially in early GNSS files. Several different combinations of codes and carrier-phases for GNSSMR as substitutes to extract sea level have been proposed;however, the requirement of these methods for application of cycle slip detection or multifrequency observations to isolate multipath signals reduces their applicability. Here, we proposea new method for sea-level estimation using signal strength indicator (SSI) data in GNSS observation files, which is an alternative to existing methods because SSI data always exist.To verify the proposed method, we used four multiGNSS data from three stations to monitor sea level. Sea-level estimations with root-mean-square errors (RMSEs) of 7–8, 5–9, 12–15 and 9–13 cm relative to in situ data were retrieved, and the correlation coefficients for these stations were bigger than 0.98, 0.98, 0.93 and 0.96, respectively. Moreover, the proposed method measures sealevels with precision similar with the traditional SNR method.In addition, sea-level results derived from the proposed method at these stations were further applied to estimate ocean tides. Oceantide coefficients for several main tides determined by different data were in good agreement.
keywords：— Global navigation satellite system multipath reflectometry (GNSS-MR), ocean tides, sea-level estimation, signal strength indicator (SSI), signal-to-noise ratio (SNR).
citation：N. Wanget al., "Sea-Level Monitoring and Ocean Tide Analysis Based on Multipath Reflectometry Using Received Strength Indicator Data From Multi-GNSS Signals," inIEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 4211513, doi: 10.1109/TGRS.2022.3219074.
