ABSTRACT
The emergence of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology has expanded the detection methods of snow depth. However, there are still two problems to be solved: On the one hand, the existence of terrain error. On the other hand, there will be geographical location errors when GNSS stations, meteorological snow depth stations and modeling snow depth stations are not co-located. Based on this, this study takes P360 and P676 stations as examples to conduct research on different satellite systems. It verifies the feasibility of the terrain tilt correction (TTC) algorithm based on different azimuth intervals of reflection area. Then, according to the distance between the meteorological snow depth station (546), modeling snow depth station (KWEY and KWYS) and GNSS station (P360 and P676), it is evaluated whether the snow depth inversion accuracy of different satellite systems is related to the distance. The experiment can lead to the following three inferences. First, a meteorological station may not be a suitable reference station due to the difference in geographical location and surrounding environment. Secondly, the modeling stations are all close to the P676 station. It can be found that the root mean square error (RMSE) of the P676 station and modeling station is small, ranging from 12-15 cm, which shows that the closer the GNSS station is to the modeling station, the higher the accuracy of snow depth retrieval, confirming the conjecture that the error may occur when the stations are not co-located. Last but not least, because of its unique frequency division multiple access (FDMA) modes, the GLONASS system shows a good snow depth inversion effect compared with the meteorological station and modeling station, which further confirms the reliability of modeling station. This study provides an important data source and experimental verification for GNSS-IR technology.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author contributions
Conceptualization, N.Z. and H.C.; Methodology, N.Z. and H.C.; Supervision, H.C.; Software, N.Z.; Funding acquisition, H.C.; Visualization, N.Z.; Project administration, H.C.; Data curation, N.Z.; Validation, H.C.; Investigation, N.Z.; Formal analysis, H.C.; Writing – original draft preparation, N.Z.; Writing – review and editing, H.C. All authors have read and agreed to the published version of the manuscript.
Data availability statement
Thanks to USDA’s NRCSNWCC (https://www.nrcs.usda.gov/wps/portal/wcc/home/snowClimateMonitoring/) for providing the meteorological snow depth observations. Thanks to NOAA’s NOHRSC Interactive Snow Information (https://www.nohrsc.noaa.gov/interactive/html/map.html) for delivering the modelling snow depth observations. Thanks to UNAVCO (https://data.unavco.org/archive/gnss/rinex3/obs/) for providing GNSS observations for the P360 and P676 stations. It is recommended to acknowledge IGS for providing precision ephemeris files of MGEX data (https://ftp.gfz-potsdam.de/GNSS/products/mgnss/).