Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

Abstract

One of the challenges for hydrologists and environmental scientists is the need to obtain and sustain in situ water level measurements for calibrating and improving forecast models, validating satellite and airborne data products, and developing early-warning flood systems. Ground-based measurements are still scarce in many regions. In particular, stream flow monitoring gauges have been declining sharply since the mid 1980s due to high maintenance cost, funding shortfalls and (geo-) political constraints (Hannah et al., 2011;Reid et al., 2019;Ruhi et al., 2016Ruhi et al., , 2018. While satellite remote sensing techniques have been utilized to monitor oceanic and land surface water with unprecedented global coverage, their measurements are associated with moderate uncertainties and temporal resolution (>7-day return) (Escudier et al., 2017;Jarihani et al., 2013). The upcoming NASA's Surface Water Ocean Topography (SWOT) satellite mission will collect high-accuracy measurements of inland surface water elevation (10 cm error for 1 km 2 areas) at unprecedented scales (10-70 m resolution) using Ka-band interferometric synthetic aperture radar. SWOT will provide global maps of water surface elevation, slope and inundated areas for rivers wider than 100 m (Biancamaria et al., 2016). The SWOT interferometric swath will pass over a given location two or three times every 21-day orbital cycle (Tuozzolo et al., 2019). However, the coarse temporal resolution of satellite altimetry missions such as SWOT and the requirement for monitoring smaller rivers and tributaries underline the significance of in situ monitoring sites. Measurements of sea surface and river water level using ground-based sensors conventionally rely on contact methods, such as traditional float and stilling well gauges (Noye, 1974) and bubbler pressure gauges (Pugh, 1972), or proximal sensing gauges, such as acoustic (

Observations of River Ice Breakup Using GNSS-IR, SAR, and Machine Learning

“…However, it should be noted that this shift in the antenna orientation degrades the performance of the antennas for positioning applications. Other studies have used similar low-cost and compact GNSS antennas for monitoring water levels [14], [15]. The motivation for using an array of GNSS antennas as opposed to a single antenna was primarily to improve the precision of GNSS-IR water level measurements by reducing the effect of random noise [16].…”

“…GNSS-IR has been used for monitoring water levels [9], lake ice [10], snow depth [11], sea ice [12], tides [13] and more. Recently, lowcost and compact GNSS-IR sensors have been developed for convenient environmental monitoring [14], [15], [16]. Moreover, the accessibility of the GNSS-IR technique has benefited from the emergence of various open-source software tools for processing GNSS-IR data [17], [18], [19].…”

Real‐Time Water Levels Using GNSS‐IR: A Potential Tool for Flood Monitoring

“…Recently it has become clear that compact, low‐cost (or mass‐market) GNSS antennas and receivers, such as those that are embedded in mobile devices, may be better suited for GNSS‐IR water level monitoring than geodetic‐standard antennas (Fagundes et al., 2021; Karegar et al., 2022; D. J. Purnell et al., 2021; Williams et al., 2020). Geodetic‐standard antennas are designed to reduce the interference from reflected GNSS signals, while the low‐cost antennas are not (it should be noted that the low‐cost antennas do not perform as well as geodetic‐standard antennas for positioning applications).…”

“…Recently it has become clear that compact, low-cost (or mass-market) GNSS antennas and receivers, such as those that are embedded in mobile devices, may be better suited for GNSS-IR water level monitoring than geodetic-standard antennas (Fagundes et al, 2021;Karegar et al, 2022;D. J. Purnell et al, 2021;Williams et al, 2020).…”

Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring

“…Climate model predictions become more reliable with an increased density of sensors, hence low-cost environmental sensor networks are emerging as a powerful tool for climate monitoring [ 2 ]. One recent innovation repurposes mass-market GNSS technology for water-level monitoring, using a technique called GNSS Interferometric Reflectometry (GNSS-IR), and has the potential to be used to increase the density of coastal water-level stations [ 3 , 4 ]. In remote regions such as Greenland or Antarctica, where sea level information is critical for climate monitoring [ 5 , 6 ], field campaigns are expensive and it may be prohibitively expensive to maintain a dense network of sensors.…”