Soil Moisture Monitorization Using GNSS Reflected Signals

TL;DR

This paper explores the use of GNSS reflected signals, including upcoming Galileo signals, for remote sensing soil moisture, highlighting recent theoretical developments and experiments that demonstrate the potential advantages of GNSS-R systems.

Contribution

It presents recent theoretical work and experimental results on dedicated GNSS-R systems for soil moisture monitoring, emphasizing the benefits of upcoming Galileo signals.

Findings

GNSS-R can effectively sense small changes in soil moisture.

Galileo signals enable multi-spectral analysis and improved inversion models.

Upcoming satellite signals increase SNR and measurement accuracy.

Abstract

The use of GNSS signals as a source of opportunity for remote sensing applications, GNSS-R, has been a research area of interest for more than a decade. One of the possible applications of this technique is soil moisture monitoring. The retrieval of soil moisture with GNSS-R systems is based on the variability of the ground dielectric properties associated to soil moisture. Higher concentrations of water in the soil yield a higher dielectric constant and reflectivity, which incurs in signals that reflect from the Earth surface with higher peak power. Previous investigations have demonstrated the capability of GPS bistatic scatterometers to obtain high enough signal to noise ratios in order to sense small changes in surface reflectivity. Furthermore, these systems present some advantages with respect to others currently used to retrieve soil moisture. Upcoming satellite navigation systems, such as the European Galileo, will represent an excellent source of opportunity for soil moisture remote sensing for various reasons. First, the existence of pilot signals will provide the possibility to extend coherent integration times, which will contribute to the increase of received signals SNR. In addition, the availability of Galileo L1 and L5 signals will allow the multi-spectral analysis of the reflected signals and the development of inversion models which will be able to account more precisely for adverse effects, such as surface roughness and vegetation canopy. In this paper we present some of the recent theoretical work and experiments carried out at Starlab focusing on the development of dedicated Soil Moisture GNSS-R systems.