Snow Depth Estimation on Slopes Using GPS-Interferometric Reflectometry

Abstract

Snow is one of the most critical sources of freshwater, which influences the global water cycle and climate change. However, it is difficult to monitor global snow variations with high spatial–temporal resolution using traditional techniques due to their costly and labor-intensive nature. Nowadays, the Global Positioning System Interferometric Reflectometry (GPS-IR) technique can measure the average snow depth around a GPS antenna using its signal-to-noise ratio (SNR) data. Previous studies focused on the use of GPS data at sites located in flat areas or on very gentle slopes. In this contribution, we propose a strategy called the Tilted Surface Strategy (TSS), which uses the SNR data reflected only from the flat quadrants to estimate the snow depth instead of the conventional strategy, which employs all the SNR data reflected from the whole area around a GPS antenna. Three geodetic GPS sites from the Plate Boundary Observatory (PBO) project were chosen in this experimental study, of which GPS sites p683 and p101 were located on slopes with their gradients up to 18% and the site p025 was located on a flat area. Comparing the snow depths derived with the GPS-IR TSS method with the snow depth results provided with the GPS-PBO, i.e., GPS-IR with the conventional strategy, the Snowpack Telemetry (SNOTEL) network measurements and gridded Snow Data Assimilation System (SNODAS) estimates, it was found that the snow depths derived with the four methods had a good agreement, but the snow depth time series with the GPS-IR TSS method were closer to the SNOTEL measurements and the SNODAS estimates than those with GPS-PBO method. Similar observations were also obtained from the cumulative snowfall time series. Results generally indicated that for those GPS sites located on slopes, the TSS strategy works better.

Insights into Very Early Afterslip Associated with the 2021 M 8.2 Chignik, Alaska Earthquake Using Subdaily GNSS Solutions

“…Based on the PPP-AR software, Precision Point Positioning (PPP) with Ambiguity Resolution (AR) is conducted for raw GNSS observations to obtain kinematic solutions. The tropospheric delay is corrected by Vienna Mapping Functions 1 (VMF1), and the high-order ionospheric error is corrected using GIM products [33][34][35]. Meanwhile, the effects of solid tide, ocean tide, and polar tide are also corrected.…”

GNSS-IR Snow Depth Retrieval Based on the Fusion of Multi-Satellite SNR Data by the BP Neural Network

“…The SNR sequence with the trend term removed is a function of the satellite elevation angle, and the residual sequence has a constant frequency after the antenna height is determined. For snow depth retrieval, the distance from the antenna phase center to the snow surface is unknown, so LSP analysis is imported to obtain the frequency at the peak of the amplitude [27]. Then, the height from the antenna phase center to the snow surface is calculated using the following equation:…”

Detection of GNSS antenna oscillatory motion and multipath conditions via exploitation of multipath-induced SNR variations

“…In recent studies, the multipath effect on GNSS satellite signals has become a tool as it is employed in mapping the multipath environment (Bilich and Larson 2007) and GNSS remote sensing applications (Roussel et al 2016;Wei et al 2019;Steiner et al 2019), where the SNR is analyzed to identify variations of (1) moisture and soil parameters (Chew et al 2014;Wu et al 2018), (2) the tides and the mean sea level (Lofgren et al 2014;Jin et al 2017) and (3) the snow height (Larson and Nievinski 2013). The principle of those studies is that the GNSS antenna remains stable for the recording period and the SNR variations are the result of the satellite orbit and the reflection point as it moves across the reflecting surface, while day-to-day variability is the result from changes of the multipath environment.…”