Use of GNSS SNR data to retrieve soil moisture and vegetationvariables over a wheat crop

This work aims to estimate soil moisture and vegetation characteristics from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected over a rainfed wheat field in southwestern France. The retrievals are compared with two independent reference datasets: in situ observations of soil moisture and vegetation height, and numerical simulations from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data. Changes in vegetation height are more likely to modulate the SNR dominant period derived from a wavelet analysis. Surface volumetric soil moisture can be estimated ( R 2  = 0.73, RMSE = 0.014 m 3  m −3 ) when the wheat is smaller than 20 cm. The quality of the estimates markedly decreases when the vegetation height increases. This is because the GNSS signal is less affected by the soil contribution. A wavelet analysis provides an accurate estimation of the wheat crop height ( R 2  = 0.98, RMSE = 6.2 cm). The latter correlates with modeled above-ground biomass of the wheat from stem elongation to ripening. It is found that the vegetation retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground biomass.