4.6 Article

(Near) Real-Time Snow Water Equivalent Observation Using GNSS Refractometry and RTKLIB

Journal

SENSORS
Volume 22, Issue 18, Pages -

Publisher

MDPI
DOI: 10.3390/s22186918

Keywords

global navigation satellite systems; GNSS; refractometry; snow water equivalent; SWE; real time; RTK; RTKLIB; emlid

Funding

  1. Swiss National Science Foundation [SNF P400P2_199328/1]

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The study explores the application of GNSS refractometry in snow water equivalent monitoring, finding that using the open-source software RTKLIB allows for quick processing and (near) real-time determination of SWE data, effectively enhancing the accuracy and reliability of snow hydrological monitoring and modeling.
Global navigation satellite system (GNSS) refractometry enables automated and continuous in situ snow water equivalent (SWE) observations. Such accurate and reliable in situ data are needed for calibration and validation of remote sensing data and could enhance snow hydrological monitoring and modeling. In contrast to previous studies which relied on post-processing with the highly sophisticated Bernese GNSS processing software, the feasibility of in situ SWE determination in post-processing and (near) real time using the open-source GNSS processing software RTKLIB and GNSS refractometry based on the biased coordinate Up component is investigated here. Available GNSS observations from a fixed, high-end GNSS refractometry snow monitoring setup in the Swiss Alps are reprocessed for the season 2016/17 to investigate the applicability of RTKLIB in post-processing. A fixed, low-cost setup provides continuous SWE estimates in near real time at a low cost for the complete 2021/22 season. Additionally, a mobile, (near) real-time and low-cost setup was designed and evaluated in March 2020. The fixed and mobile multi-frequency GNSS setups demonstrate the feasibility of (near) real-time SWE estimation using GNSS refractometry. Compared to state-of-the-art manual SWE observations, a mean relative bias below 5% is achieved for (near) real-time and post-processed SWE estimation using RTKLIB.

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