# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 1 Introduction  
The Arctic region, one of the regions with the most significant temperature increases, has exhibited a significant response to climate change [1], and the snow and glaciers in Svalbard have changed sharply in recent years [2; 3]. Snow is a critically important and rapidly changing feature of the Arctic [4], but snow depth observations in the Arctic are still insufficient in terms of quantity and quality. Ultrasonic sensors can only obtain the snow depth at certain points, and the discreteness and uneven distribution of sites severely restrict the real-time acquisition and application of snow information [5]. Remote sensing methods can obtain large-scale snow information, but the accuracyis seriously affected by factors such as cloud and atmospheric refraction and they are incapable of directly obtaining snow depth information [6]. With the rapid development of global navigation satellite system interferometric reflectometry (GNSS-IR) technology, it has received increasing attention from the remote-sensing community and is applied in many research fields, including the monitoring of sea-surface wind [7], soil moisture [8; 9] and snow depth [10; 11; 12; 13]. At low satellite elevation, the antenna of a GNSS station will receive both the direct signal transmitted by the satellite and the signal reflected by objects around the antenna. The two signals will interfere with each other and cause a deviation called the multipath error. Since the reflected signal received by the antenna carries reflector information, the physical characteristics of the reflector can be determined by accurately extracting the reflected signal from the original signal. In the field of snow depth estimation with the signal-to-noise ratio (SNR), [PERSON] first proposed the feasibility of using SNR observations to detect the height of the reflecting surface [14] and found a good correlation between the snow depth obtained by GPS L1 SNR observations and ultrasonic snow depth measurements [15]. [PERSON] inferred both snow depth and snow density for a snow-covered ground reflector using GPS multipath signals and obtained an estimate of the snow water equivalent [16]. [PERSON] applied snow depth estimation based on SNR observations to a complex terrain, and the results showed that GPS stations with poor observation quality, such as those in forests, were not suitable for snow depth measurement [17]. [PERSON] proposed forward/reverse models based on GPS multipath reflected signals, and parameterized unknown snow characteristics were used to estimate snow depth variation [18]. [PERSON] combined GPS and GLONASS SNR observations to estimate snow depth, and the results of the two systems were comparable in terms of accuracy [19]. [PERSON] used GPS and BDS SNR observations in Ny-Alesund for snow depth estimation and found that the accuracy of GPS is slightly higher than that of BDS due to the difference between the number of visible satellites and the spatial distribution of the two constellations [20]. [PERSON] used GPS SNR observations collected by a glacier-observation station and successfully determined the variation in snow depth, providing a new data source for the study of glacier mass balance [21]. [PERSON] investigated the topographic conditions in snow depth estimation and proposed a strategy to improve the results [22]. At the same time, [PERSON] and [PERSON] used GPS geometry-free observations (L4) for multipath analysis for the first time and then derived snow depth variation. The results were worse than those of SNR observations due to the influence of the ionosphere [23]. [PERSON] successfully obtained snow depths and snow temperatures using the non-parametric bootstrapping model and L4 observations [24]. [PERSON] analyzed the estimation difference between the combination of the dual-frequency carrier phase and the combination of the single-frequency phase with a pseudorange.