Citation: Su, M.; Chang, X.; Zheng, F.;
Shang, J.; Qiao, L.; Teng, X.; Sun, M.
Theory and Experiment Analysis on
the Influence of Floods on a GNSS
Pseudo-Range Multipath and CNR
Signal Based on Two Cases Study in
China. Remote Sens. 2022, 14, 5874.
https://doi.org/10.3390/rs14225874
Academic Editors: Kamil Krasuski
and Damian Wierzbicki
Received: 8 October 2022
Accepted: 17 November 2022
Published: 19 November 2022
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Article
Theory and Experiment Analysis on the Influence of Floods
on a GNSS Pseudo-Range Multipath and CNR Signal Based
on Two Cases Study in China
Mingkun Su
1
, Xin Chang
2
, Fu Zheng
3
, Junna Shang
1
, Lei Qiao
1
, Xuyang Teng
1
and Minhong Sun
1,
*
1
Communication Engineering School, Hangzhou Dianzi University, Hangzhou 310005, China
2
School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China
3
School of Electronic Information Engineering, Beihang University, Beijing 100191, China
* Correspondence: cougar@hdu.edu.cn
Abstract:
The surrounding environment of a GNSS observation station is changed during a flood,
and this results in a more serious multipath than in a normal environment. Considering that the
multipath error is largely related to the pseudo-range multipath and CNR (Carrier-to-noise ratio)
of the GNSS signal, the influence of floods on a pseudo-range multipath and CNR is analyzed in
theory and through experiment. To ensure the accuracy of the analysis results, the ground track
repeat period of GPS, GLONASS, and BDS satellites is investigated from the perspective of theory
and skyplots. Two real cases study collected in Zhengzhou and Xinxiang, China, in 2021, are used to
demonstrate the influence of floods on a pseudo-range multipath and CNR in detail. Experimental
results show that the pseudo-range multipath of a GPS satellite performs more seriously during
a flood. The maximum RMS increase rate is approximately 17.85%, and the average of all other
satellites with a whole arc is approximately 6.55%. In addition, the CNR of three GNSS systems
performs a decrease during a flood. For GPS and GLONASS satellites, the decrease performs more
seriously at a high elevation angle than that at a low elevation angle. The maximum decrease is
approximately 5 dB-Hz for the GPS satellite and approximately 7 dB-Hz for the GLONASS satellite.
In terms of the BDS system, the CNR of all three orbital type satellites decreases during a flood. The
average decrease is approximately 2 dB-Hz for BDS MEO and GEO satellites, and about 1 dB-Hz for
the BDS IGSO satellite.
Keywords:
GNSS; ground track repeat period; pseudo-range multipath; carrier-to-noise ratio; flood
1. Introduction
Regarding global warming, extreme weather has been occurring more frequently in
recent years, especially floods and urban water logging caused by heavy rainfall. Floods or
urban water-logging disasters can result in many serious disturbances to communities and
the environment [
1
,
2
]. Thus, many researchers have been dedicating their efforts to detect
and forecast floods. However, the flood having an effect on the GNSS (Global Navigation
Satellite System) signal and the mechanism of influence have not been researched in depth.
The surrounding environment of the observation station is changed during a flood,
and this change leads to a fluctuation of multipath effects in theory. Cai et al. investigated
the influence of different observation environments on the multipath effect [
3
]. The results
show that the pseudo-range multipath (MP) with water around the observation station
is increased for the BDS (BeiDou System) and GPS (Global Positioning System) satellites
compared with the open sky. However, the water environment of this experiment only
exists on one side of the observation station. Thus, it can be predicted that the multipath
error will become more severe when a flood occurs, since the observation station will be
surrounded by water. In addition, Michael indicated that the reflection coefficient of fresh
water is nearly three times larger than that of dry soil [
4
]. However, how much the change
Remote Sens. 2022, 14, 5874. https://doi.org/10.3390/rs14225874 https://www.mdpi.com/journal/remotesensing
