# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 5 Conclusions  
In this study, the potential use of Sentinel-1 radar signals for the assessment of different surface states (soil moisture and soil roughness, vegetation cover parameters) is analyzed. The results are based on the use of a large database, describing two agricultural years of cereal production (2015-2016 and 2016-2017). Remotely sensed data recorded by the Sentinel-1 and Landsat payloads are compared with in situ measurements, corresponding to more than 20 reference plots, which are characterized either by bare soil, or by cereal coverage. This study has made it possible to determine the sensitivity of Sentinel-1 signals to soil moisture. The radar sensitivity is clearly determined, through the interpretation of data corresponding to three classes of vegetation, ranging from bare soil to dense canopies. The radar signal sensitivity is found to decrease with increasing NDVI index, and to be higher in the VV polarization than in the VH polarization. This suggests that VV data have a greater potential for the estimation of soil moisture. Concerning the soil roughness, the Sentinel-1 VV and VH radar signals are found to be highly sensitive to the roughness parameter Hrms, with a dynamic range reaching 6 dB for both polarizations. A correlation greater than 0.7 is determined for an empirical logarithmic relationship between the radar signals and Hrms, for the case of plots in the low moisture class (mv \(\leq\) 10 vol. %). Two classes of moisture (mv \(\leq\) 15 vol. %, mv \(>\) 15 vol. %) are used to analyze the role of vegetation cover, revealing that the VV radar signals are highly sensitive to the LAI. The dynamic range of the radar signals is equal to 2 dB and 1.5 dB, for these two classes of soil moisture. There is almost no significant correlation between signal strength and soil moisture in the case of the VH polarization. The radar signals expected over bare soils are modeled using a simple empirical expression, which includes a linear relationship with respect to soil moisture, and a logarithmic behavior with respect to roughness. When the calibrated model is validated with real data recorded over a variety of reference fields, it is found to be very accurate: the RMSE is equal to 1.27 and 1.23 dB, for the VV and VH polarizations, respectively. In the case of radar signals observed over covered soils, a calibrated water cloud model is proposed, using the NDVI as a parameter to describe the vegetation. This model is proposed for the VV polarization only. As in the case of bare soils, when the model is validated with a set of reference fields, the simulated signal strengths are found to be in good agreement with the real measurements (RMSE equal to 0.84 dB). Despite its simplicity, the water cloud model is able to correctly describe the behavior of radar signals, as a function of soil moisture and vegetation parameters. The empirical calibration parameters derived from this analysis have been adapted to the case of agricultural plots with a cereal cover. In future studies, they will be tested at a different site, characterized by different types of vegetation cover. This study reveals the high potential of Sentinel-1 data, when combined in synergy with optical (Landsat) images, for the recovery of moisture and vegetation characteristics. It is clearly shown that  
Figure 11: Water cloud model validation for the VV polarization. VV polarized signals have a greater sensitivity to these surface parameters, and that remotely sensed Sentinel-1 radar data can be inverted and used for the mapping of soil and cereal cover characteristics. This study was funded by the ANR AMETHYST (ANR-12 TMED-0006-01) and TOSCA/CNES ASCAS projects. We extend our warm thanks to the technical teams at the IRD and INAT (Institut National Agronomique de Tunisie) and [PERSON] for their invaluable collaboration and support during the ground-truth measurement campaigns and data processing.