Abstract
Soil moisture is a very important variable in the study of terrestrial water and energy cycle, traditional soil moisture monitoring methods have great limitations for high resolution large area monitoring. Soil moisture monitoring based on Global Navigation Satellite System Interferometry and Reflectometry (GNSS-IR), which also known as signal-to-noise ratio (SNR) method or interference pattern technique (IPT), can be used to overcome those shortcomings, so that it receives more and more attention in recent researches. However, previous studies mostly used the observed data of Global Positioning System (GPS) to estimate soil moisture. With the maturity of the Beidou system, application of Beidou system is the focus of future research. This paper studies the soil moisture inversion methodology using the signal-to-noise ratio (SNR) data of Beidou B1, B2, and also proposes a novel method of soil moisture inversion based on geometry-free linear combinations of carrier phase on Beidou B1, B2. It also presents the soil moisture inversion model as well as the relevant signal processing flow. Moreover, an in-situ experiment campaign is performed for verification. The results show that the soil moisture obtained by the proposed models is significantly correlate to the ground truth data. The signal-to-noise ratio method achieves slightly higher accuracy than the carrier phase combinations method, which proves that both the two methods can realize a continuous long-term in-situ observation of soil moisture.
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Acknowledgments
The authors acknowledge the financial support of the National Natural Science Fund of China (Grant Nos. 31971781 and 41774028). The authors also would like to extend their sincere gratitude to Dr. Nazi Wang from Shandong University (Weihai) for her help in data processing.
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Sun, B., Yang, L., Wu, X., Wang, C., Guo, X., Zhang, L. (2020). Soil Moisture Inversion Based on Beidou SNR and Carrier Phase Combinations. In: Sun, J., Yang, C., Xie, J. (eds) China Satellite Navigation Conference (CSNC) 2020 Proceedings: Volume I. CSNC 2020. Lecture Notes in Electrical Engineering, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-15-3707-3_6
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DOI: https://doi.org/10.1007/978-981-15-3707-3_6
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