Abstract
Snow is a critical component of the climate system and a key storage component in the hydrological cycle. Under the condition of global warming, changes in snow cover not only influence the water runoff, but may also affect the local ecosystem. Therefore, how to accurately measure the depth of snow and predict melting rate plays an important role in studying ecological changes and flood control system. However, the traditional ground-based observation method has low temporal and spatial resolution and cannot observe in real time. In recent years, with the continuous development of GNSS (Global Navigation Satellite System), GNSS-IR (GNSS interferometric reflectometry) technology, which does not require the use of special receivers and inverts surface environmental information based on ordinary geodetic receivers, has become a hot research issue. In this paper, the principle of GNSS based on SNR (signal-to-noise ratio) observation using GNSS-IR technology to detect snow depth is given in detail firstly, then the feasibility and the accuracy of GNSS-IR technology for detecting snow depth is analyzed by using Lomb-Scargle spectrum analysis method with related date by several GPS satellites. The results show that the value of the inversion is in good agreement with the measured value. The accuracy of the technique for snow-depth detection can reach about 6 cm, which can better reflect the snow depth and the variation of snow depth with time. The application of this technology is of great significance for extending the field of GNSS application and for the observation of high spatial and temporal resolution of snow depth. It has important reference value for inverting other types of surface environmental monitoring information.
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Zhu, Y., Shen, F. (2019). Snow Depth Determination Based on GNSS-IR. In: Sun, J., Yang, C., Yang, Y. (eds) China Satellite Navigation Conference (CSNC) 2019 Proceedings. CSNC 2019. Lecture Notes in Electrical Engineering, vol 562. Springer, Singapore. https://doi.org/10.1007/978-981-13-7751-8_10
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DOI: https://doi.org/10.1007/978-981-13-7751-8_10
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