Soil Moisture Monitorization Using Galileo Reflected Signals
Egido, A.; Ruffini, G.; Caparrini, M.; Martin, C.; Farres, E.; Banque, X.
Starlab Barcelona S.L.

The use of GNSS signals as a source of opportunity for remote sensing applications has been a research area of interest for more than a decade. This technique is commonly known as GNSS-R. Several applications based on a GNSS bistatic radar configuration have already been developed taking advantage of the high availability and stability of GNSS signals.

GNSS-R studies and investigations have been mainly focused on ocean altimetry and sea surface topography. However, the application of GNSS-R to land remote sensing has been largely overlooked. Nevertheless, there is experimental evidence that GPS reflected signals from the ground can be detected and processed in order to obtain soil moisture estimates.

Soil moisture is a prime parameter for the surface hydrology cycle since it affects the evapotranspiration and the heat storage ability of the soil, as well as determines the possibility of surface runoff after rainfalls. Despite the recognised relevance of soil moisture, providing such parameter over global scales remains a significant challenge. Sensors based on GNSS-R offer this possibility and could be a very important milestone in the development of a global soil moisture model.

The basis for the retrieval of soil moisture with GNSS-R systems lays in 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 incur 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. First, GNSS signals lie in L band, which is the most sensitive band for soil moisture microwave remote sensing. Secondly, variations on thermal background do not contaminate GNSS reflected signals as much as for other remote sensing techniques, such as radiometry. And thirdly, GNSS scatterometry from space has a potential higher spatial resolution than microwave radiometry, due to the highly stable carrier and code modulations of the incident signals which enables the use of Delay Doppler Mapping.

GNSS signals are direct sequence spread spectrum signals. In order to detect them, the received signal is cross correlated with a clean replica of the pseudo-random noise code that modulates the signal, resulting in complex signal waveforms. The ratio between the direct and reflected signal waveforms is the basic observable of GNSS-R systems. This provides immunity to atmospheric and propagation conditions that could eventually disturb the measurements. However, in order to be able to obtain accurate soil moisture estimates there are several effects that need to be taken into consideration. Some of those are mainly due to diffuse scattering effects over the soil surface, for instance effects due to surface roughness, vegetation canopy, and noise. To be able to account for them precisely, extensive in-situ calibration and validation campaigns should be performed.

The Galileo signal will represent an excellent source of opportunity for soil moisture remote sensing for various reasons. 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 the effects of surface roughness and vegetation canopy.

In this paper we present some of the recent theoretical work and experiments at Starlab focusing on the development of dedicated Soil Moisture GNSS-R systems.