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

Abstract

Abstract. Per capita arable land is decreasing due to the 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 paper, the Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique was used to monitor soil moisture during 66 d, from 3 December 2018 to 6 February 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 that the GNSS observations were made, soil samples taken at 5 cm depth were used for soil moisture determination to establish a reference data set. 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 coefficient between 0.7 and 0.85.

Python software to transform GPS SNR wave phases to volumetric water content

“…The data we provided with the software are part of an experiment performed in the installations of the Cajamar Center of Experiences, Paiporta, Valencia, Spain, by Martín et al (2020a). The experiment was performed from December 3, 2018 to February 6, 2019.…”

“…Using the global navigation satellite system (GNSS) signalto-noise (SNR) observables to monitor soil moisture, also called GNSS interferometric reflectometry (GNSS-IR), is a well-known technique. For comprehensive literature on GNSS-IR, refer to Larson et al (2008aLarson et al ( , 2008b, Larson et al (2010), Larson and Nievinski (2013), Chew et al (2014Chew et al ( , 2015Chew et al ( , 2016, Small et al (2016), Wan et al (2015), Chen et al (2016), Roussel et al (2016), Vey et al (2016), Zhang et al (2017), Roesler and Larson (2018), or Martín et al (2020a).…”

Development and Application of Cell-Phone-Based Internet of Things (IoT) Systems for Soil Moisture Monitoring

“…It also plays a significant role in affecting critical physical processes in numerical weather prediction, climate modeling, agricultural crop growth modeling, and flood forecasting [2]. Soil moisture is an essential component of the hydrologic cycle and a critical variable for optimizing efficient irrigation water management in agriculture [3]. Therefore, obtaining accurate real-time estimates of spatial and temporal variations in soil moisture are critical [4].…”

“…signals [1] [13], Global Navigation Satellite System (GNSS) [3], and distributed temperature sensing (DTS) [1] [14] [15] [16].…”

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

“…To do this, in situ observations are needed as a reference data set. Those observations can be obtained from conventional water content reflectometer sensors (Vey et al 2016;Larson et al 2010) or from soil data samples (Martín et al 2020). Considering a linear relationship between GNSS-IR estimated phase variations and reference VWC variations, several weeks (or months) of both types of data are necessary to obtain a good relationship.…”