In this paper the results obtained from an experiment focused on the capabilities of GNSS-R sensors for land applications
are described. This experiment was carried out within the framework of two ESA projects devoted to the investigation of
GNSS-R signal over land: LEiMON(Land Monitoring with Navigation Signals) and GRASS (GNSS Reflectometry
Analysis for BiomaSS Monitoring). The latter project consisted in the analysis of GNSS-R signal collected by a dual
polarization sensor installed on an aircraft which flew over a test area in Italy with agricultural fields and poplar plots.
It has been observed that the LR reflection coefficient was sensitive to changes in the surface soil moisture, with a total
variation of about 6 dB between the dry and wet seasons, within an interval between -8/-17 dB.Whereas, the RR
reflection coefficient was generally very low for all surfaces, in the range -20/-25 dB, with an increasing trend with
incidence angle. LR reflection coefficient was directly related to the main parameters of soil and vegetation, namely soil
moisture and vegetation biomass and rather good sensitivity to these parameters was observed. The sensitivity to soil
moisture was of about 0.25dB/%soil moisture. These results have been compared with those obtained in the LEiMON
project showing a good agreement. A clear correlation was also observed between LR reflection coefficient and poplar
biomass, especially at steep incidence angles (17-23°). The observed sensitivity was of about 1.0dB/(50-100t/ha of dry
These results have been subsequently compared with those obtained at the same frequency (L-band) with SAR sensors.
From the comparison it was observed that the sensitivity of GNSS-R signal is generally lower than that one of SAR,
except for the case of forest biomass. The obtained results suggest good prospects of GNSS-R especially for soil
moisture and forest biomass monitoring, also in view of the increasing availability of GNSS constellations and the
potential synergy with other Earth Observation sensors like SAR’s.