Soil Moisture &amp; Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

Botteron, Cyril; Dawes, Nicholas; Leclère, Jérôme; Skaloud, Jan; Weijs, Steven; Farine, Pierre-André

doi:10.3390/rs5073516

Cited by 19 publications

(9 citation statements)

References 117 publications

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“…Gutmann et al (2012) evaluated this technique successfully for an entire winter season. Boniface et al (2015) and Larson (2016) used networks of geodetic stations to derive HS around the stations at numerous locations in the western U.S. Botteron et al (2013); Cardellach et al (2011) andJin et al (2014) give an overview on several GNSS reflectometry approaches. Even though obstacles might influence the measurements, Vey et al (2016) reported that this technique might also be applicable for urban areas.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Koch

Henkel

Appel

et al. 2019

Water Resources Research

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For numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validation measurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R2)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Koch

Henkel

Appel

et al. 2019

Water Resources Research

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“…Even specific GNSS-R space-based missions have been planned by different space agencies [3,16,17]. Furthermore, more remote sensing applications have been also proposed and validated through experimental campaigns, such as: soil moisture monitoring [7,18,19,20,21,22,23,24,25], snow monitoring [26,27,28,29,30,31,32], sea-ice monitoring [33,34], ground and calm water altimetry [35,36,37,38,39,40,41,42,43] and biomass estimation [44,45]. …”

Section: Introductionmentioning

confidence: 99%

Derivation of the Cramér-Rao Bound in the GNSS-Reflectometry Context for Static, Ground-Based Receivers in Scenarios with Coherent Reflection

Ribot

Botteron

Farine

2016

Sensors

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The use of the reflected Global Navigation Satellite Systems’ (GNSS) signals in Earth observation applications, referred to as GNSS reflectometry (GNSS-R), has been already studied for more than two decades. However, the estimation precision that can be achieved by GNSS-R sensors in some particular scenarios is still not fully understood yet. In an effort to partially fill this gap, in this paper, we compute the Cramér–Rao bound (CRB) for the specific case of static ground-based GNSS-R receivers and scenarios where the coherent component of the reflected signal is dominant. We compute the CRB for GNSS signals with different modulations, GPS L1 C/A and GPS L5 I/Q, which use binary phase-shift keying, and Galileo E1 B/C and E5, using the binary offset carrier. The CRB for these signals is evaluated as a function of the receiver bandwidth and different scenario parameters, such as the height of the receiver or the properties of the reflection surface. The CRB computation presented considers observation times of up to several tens of seconds, in which the satellite elevation angle observed changes significantly. Finally, the results obtained show the theoretical benefit of using modern GNSS signals with GNSS-R techniques using long observation times, such as the interference pattern technique.

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“…Assuming that the antenna position is known, we can compute the position of the surface of reflection. This approach provides precise localization and dating of the measures that allows for spatio-temporal comparison of water levels and snow cover, respectively [5–8]. These parameters are very important for flood monitoring and avalanche prevention, as well as for hydroelectric companies.…”

Section: Introductionmentioning

confidence: 99%

Normalized GNSS Interference Pattern Technique for Altimetry

Ribot

Kucwaj

Botteron

et al. 2014

Sensors

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It is well known that reflected signals from Global Navigation Satellite Systems (GNSS) can be used for altimetry applications, such as monitoring of water levels and determining snow height. Due to the interference of these reflected signals and the motion of satellites in space, the signal-to-noise ratio (SNR) measured at the receiver slowly oscillates. The oscillation rate is proportional to the change in the propagation path difference between the direct and reflected signals, which depends on the satellite elevation angle. Assuming a known receiver position, it is possible to compute the distance between the antenna and the surface of reflection from the measured oscillation rate. This technique is usually known as the interference pattern technique (IPT). In this paper, we propose to normalize the measurements in order to derive an alternative model of the SNR variations. From this model, we define a maximum likelihood estimate of the antenna height that reduces the estimation time to a fraction of one period of the SNR variation. We also derive the Cramér–Rao lower bound for the IPT and use it to assess the sensitivity of different parameters to the estimation of the antenna height. Finally, we propose an experimental framework, and we use it to assess our approach with real GPS L1 C/A signals.

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Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

Cited by 19 publications

References 117 publications

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Derivation of the Cramér-Rao Bound in the GNSS-Reflectometry Context for Static, Ground-Based Receivers in Scenarios with Coherent Reflection

Normalized GNSS Interference Pattern Technique for Altimetry

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