# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 1 Introduction  
Soil surface and vegetation cover play a key role in various processes at the soil-vegetation-atmosphere interface, such as evapotranspiration, infiltration and runoff [1; 2; 3; 4]. They are also essential characteristics in agricultural contexts, since they can allow improved estimations of crop conditions and requirements to be derived, and can also facilitate the optimized management of irrigation. Surface parameters have long been based on field measurements that do not allow their spatio-temporal variability to be retrieved. In this context, various studies based on optical and radar remote sensing have been proposed in the last 30 years in an effort to better characterize surface conditions and characteristics [5; 6]. At present, various moisture products are proposed on a global operational basis. These are derived from microwave radiometric measurements (such asSMOS, AMSR-E) and/or scatterometer measurements (e.g., ASCAT), with a spatial resolution ranging between 10 and 40 km [7; 8]. At the scale of individual agricultural fields, the monitoring of spatio-temporal variations in the vegetation's biophysical parameters, together with the soil's surface characteristics (water content in particular) can provide farmers with key information for the irrigation and management of their crops. Several methods have been developed to retrieve soil surface characteristics, especially surface roughness and soil moisture, from high (fine) spatial resolution synthetic aperture radar (SAR) data [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. These techniques were considerably enriched by the launch of more advanced SAR sensors in the 2000s--ASAR/ENVISAT, RADARSAT, TerraSAR-X, and so forth--and make use of various types of algorithmic methods, including neural networks, look-up tables, and the inversion of physical or empirical models. In the case of bare soils, the radar signal scattered by the surface depends on the roughness, as well as the dielectric constant of the first centimeters of soil. The latter is directly related to the soil's water content and the frequency of the radar sensor. The corresponding depth ranges from approximately 5 cm in the L-band to 1 cm in the X-band, in soils with a high water content [21]. Several studies have analyzed the behavior of the radar signal as a function of soil moisture content. Stronger signal sensitivity is observed at low incidence angles, between 20\({}^{\circ}\) and 35\({}^{\circ}\), than at high incidence angles [22; 23]. Different inversion approaches have been proposed, based on empirical or physical radar backscattering models ([PERSON] model [24], IEM model [25], Dubois model [26], Baghdadi model [27], etc.). Inversion studies generally report an accuracy ranging between 3 and 6 vol. % in the case of bare soils [28; 29; 30; 31; 32; 33]. Unlike the case of soil moisture, soil roughness estimations are most accurate when they are made at high angles of incidence [34], and the dynamic range of the backscattered radar signal associated with this parameter decreases as a function of increasing radar frequency [35]. The soil roughness description is based mainly on two statistical parameters: the root mean square surface height (Hrms) and the correlation length of soil height variations (L). Different approaches have been proposed to improve the roughness description [36; 37; 38; 39; 40]. Most experimental inversion studies have been restricted to the evaluation of just two or three roughness classes (smooth, medium, high) [41]. For surfaces with a vegetation cover, the backscattered radar signal depends on the characteristics of the soil and vegetation, as well as the radar configurations used for the observations. Various models have been developed in an effort to accurately retrieve soil parameters and to gain a clear understanding of the behavior of soil and vegetation components in the radar total backscattering coefficient [35]. These models are based primarily on solving the radiative transfer equation.