Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa

Vey, Sibylle; Güntner, Andreas; Wickert, Jens; Blume, Theresa; Ramatschi, Markus

doi:10.1007/s10291-015-0474-0

Cited by 66 publications

(62 citation statements)

References 25 publications

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“…where e is satellite elevation angle, h is antenna height and λ is carrier wavelength [23]. For a given SNR trace, the sensing footprint is the total area of First Fresnel zones at different elevation angles.…”

Section: Satellite Elevation Effectsmentioning

confidence: 99%

Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data

2016

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Snow is a water resource and plays a significant role in the water cycle. However, traditional ground techniques for snow monitoring have many limitations, e.g., high-cost and low resolution. Recently, the new Global Positioning System-Reflectometry (GPS-R) technique has been developed and applied for snow sensing. However, most previous studies mainly used GPS L1C/A and L2C Signal-to-Noise Ratio (SNR) data to retrieve snow depth. In this paper, snow depth variations are retrieved from new weak GPS L2P SNR data at three stations in Alaska and evaluated by comparing with in situ measurements. The correlation coefficients for the three stations are 0.79, 0.88 and 0.98, respectively. The GPS-estimated snow depths from the L2P SNR data are further compared with L1C/A results at three stations, showing a high correlation of 0.94, 0.93 and 0.95, respectively. These results indicate that geodetic GPS observations with SNR L2P data can well estimate snow depths. The samplings of 15 s or 30 s have no obvious effect on snow depth estimation using GPS SNR L2P measurements, while the range of 5˝-35˝elevation angles has effects on results with a decreasing correlation of 0.96 and RMSE of 0.04 m when compared to the range of 5˝-30˝with correlation of 0.98 and RMSE of 0.03 m. GPS SNR data below 30˝elevation angle are better to estimate snow depth.

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Section: Satellite Elevation Effectsmentioning

confidence: 99%

Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data

2016

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“…Previous studies focused on snow depth estimation from the new civilian L2C signal, which has a higher signal power, compared the L2P signal [24]. In 2009, there were only 7 satellites ( PRN 5,7,12,15,17,29, 31 from GPS block IIRM) transmitting the L2C signal and 24 satellites transmitting L2P. With the installation of block IIF, 10 additional L2C transmitters were sent into the orbit until today ( PRN 1,3,6,8,9,24,25,26,27,30).…”

mentioning

confidence: 99%

Monitoring Snow Depth by GNSS Reflectometry in Built-up Areas: A Case Study for Wettzell, Germany

Vey

Güntner

Wickert

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Snow storage dynamics is essential to predict floods, to quantify water resources for human use and irrigation, and to assess the risk of avalanches. Recently, Global Navigation Satellite System (GNSS) ground stations have been successfully used to continuously estimate snow depth at an intermediate scale of about 1.000 m 2 around the stations. In this study, GNSS signal-to-noise ratio (SNR) data at the station Wettzell, Germany, are used to estimate snow depth variations from 2012 to 2015. The station Wettzell is located in a built-up area. The most challenging task at this site is to separate the GNSS reflections from the ground and from surrounding buildings. We modified the interference approach previously used for snow depth estimation by using the phase of the multipath interference pattern instead of their frequency. Additionally, we complemented the analysis by including satellites transmitting the L2P signal into the processing. We studied the performance of the L1 signal. The derived GNSS snow depth ranges between 3 cm and 25 cm and corresponds well to in-situ observations by an ultra-sonic sensor, with a correlation of 0.8 for daily time series. The residuals of GNSS snow depths compared to the ultra-sonic sensor reveal a RMSE of 4.3 cm for the L2 and 5.9 cm for the L1 signal with a small bias of 1 cm. The results show that the existing data of the global GNSS tracking network promises to provide valuable complementary snow depth observations to the existing sensors at several hundred sites worldwide, including urban areas.

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“…Commercially available geodetic-quality GNSS receivers and antennas can be used for GNSS-IR. The method has been tested with the Global Positioning System (GPS) satellite constellation, and it has been shown to provide consistent measurements of upper surface soil moisture content, (Larson et al, 2008a(Larson et al, , 2008b(Larson et al, , 2010Larson and Nievinski, 2013;Chew et al, 2014Chew et al, , 2015Chew et al, , 2016, Vey et al, 2015Wan et al, 2015;Chen et al, 2016;Zhang et al, 2017).…”

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confidence: 99%

“…The GNSS-IR footprint for a single rising or setting satellite is an elongated ellipse in the direction of the satellite track (Fresnel ellipse or zones; Larson et al, 2010;Wan et al, 2015;Vey et al, 2015;Roesler and Larson, 2018). As the satellite rises and the elevation angle increases, the Fresnel zone becomes smaller and closer to the GNSS antenna.…”

mentioning

confidence: 99%

“…With the use of the GPS constellation, the GPS-IR reflection footprint is far from homogeneous, as shown in Fig. 2, and some tracks cannot be included in the process and analysis (Vey et al, 2015;Chew et al, 2016). Therefore, GPS-IR needs to evolve to Global Navigation Satellite System reflectometry, GNSS-IR, where multi-constellation observation provides the solution.…”

mentioning

confidence: 99%

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Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

Martín¹,

Ibáñez²,

Baixauli³

et al. 2019

Preprint

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Abstract. Per capita arable land is decreasing due to rapidly increasing population, and fresh water is becoming scarce and more expensive. Therefore, farmers should continue to use technology and innovative solutions to improve efficiency, save input costs, and optimise environmental resources (such as water). In the case study presented in this manuscript, the GNSS-IR technique was used to monitor soil moisture during 66 days, from December 3, 2018, to February 6, 2019, in the installations of the Cajamar Centre of Experiences, Paiporta, Valencia, Spain. Two main objectives were pursued. The first was the extension of the technique to a multi-constellation solution using GPS, GLONASS, and GALILEO satellites, and the second was to test whether mass-market sensors could be used for this technique. Both objectives were achieved. At the same time the GNSS observations were made, soil samples taken at 5 cm depth were used for soil moisture determination to establish a reference dataset. Based on a comparison with that reference data set, all GNSS solutions, including the three constellations and the two sensors (geodetic and mass-market), were highly correlated, with a correlation coefficients between 70 % and 85 %.

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Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa

Cited by 66 publications

References 25 publications

Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data

Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data

Monitoring Snow Depth by GNSS Reflectometry in Built-up Areas: A Case Study for Wettzell, Germany

Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

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