Multipath is one of the major source of errors in precise Global Navigation Satellite System positioning. With the emergence of new navigation systems, such as Galileo, upgraded signals are progressively being used and are expected to provide greater resistance to the effects of multipath compared to legacy Global Positioning System (GPS) signals. The high quality of Galileo observations along with recent development of the Galileo space segment can therefore offer significant advantages to Galileo users in terms of the accuracy and reliability of positioning. The aim of this paper is to verify this hypothesis. The multipath impact was determined both for code and phase measurements as well as for positioning results. The code multipath error was determined using the Code-Minus-Carrier combination. The influence of multipath on phase observations and positioning error was determined using measurements on a very short baseline. In addition, the multipath was classified into two different types: specular and diffuse, using wavelet transform. The results confirm that the Galileo code observations are more resistant to the multipath effect than GPS observations. Among all of the observations examined, the lowest values of code multipath errors were recorded for the Galileo E5 signal. However, no advantage of Galileo over GPS was observed for phase observations and for the analysis of positioning results.

Among the different approaches to the use of surface-reflected global navigation satellite systems (GNSSs) signals in Earth sciences is GNSS interferometric reflectometry (GNSS-IR), which allows the measurement of the distance between a GNSS antenna and a reflecting surface by analyzing the time-series variations of the signal-to-noise ratio parameter. This method can be used to monitor changes in the height of the reflecting surface, including the depth of snow cover. The study presents, for the first time, the application of GNSS-IR to snow cover monitoring in the Svalbard region, based on data from three monitoring stations of the Cryosphere Integrated Observatory Network on Svalbard network. The article presents the methodology for data processing and determination of the snow depth parameter and validation of the obtained results based on in-situ measurements. In addition, the study includes a detailed analysis of the suitability of the most frequently used observation types in all four multi-GNSS constellations. For the one-year period studied (between September 2023 and August 2024), the mean error of snow depth estimation was on the order of a few centimeters, with a median of daily standard deviations in the range of 2.4–5.4 cm and correlation coefficients with manual and automatic measurements in the range of 0.83–0.93. A similar suitability for estimating the snow depth parameter was observed for all tested types of multi-GNSS observations, with a slight advantage for L5 signals. This case study enables the enhancement of polar research conducted by field scientists by offering GNSS-IR as an automated alternative to traditional manual snow depth measurements.