# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 1 Introduction
The most commonly used model is the semi-empirical water cloud model [42], which considers the total signal to be the sum of contributions from the vegetation cover, together with a second contributor related to the soil, attenuated by its vegetation cover. Experimental studies using this model have shown that the vegetation-related terms in the model could be derived from different vegetation parameters, such as the vegetation water content or the leaf area index (LAI) [43; 44]. In this context, remote sensing in the visible and infrared (i.e., \"optical\") spectral ranges has demonstrated its potential for the estimation of these vegetation parameters [45; 46]. Several studies have considered different satellite indices, in particular the normalized difference vegetation index (NDVI), for the estimation of the LAI of different crop types (such as wheat, grassland, rice, corn, maize, or orchards) [47; 48]. When it is used in synergy with optical data, SAR imagery can thus provide valuable estimations of both soil moisture and vegetation parameters [30; 44; 48]. [PERSON] et al. [30] estimated soil moisture using ASAR images (C-band) in synergy with SPOT-5 optical images recorded over wheat plots, using the Water Cloud model, with an accuracy of approximately 0.06 m\({}^{3}\)/m\({}^{3}\). [PERSON] et al. [48] estimated the soil moisture over irrigated wheat plots using TerraSAR-X (TSX), Radarsat-2 and Advanced Land Observing Satellite (ALOS)/Palsar data coupled with Formosat-2 and Satellite Pour l'Observation de la Terre (SPOT)-4/5 images. [PERSON] et al. [44] combined X-band data retrieved from TSX with data derived from CosmoSky-Med (CSK) sensors, as well as NDVI, LAI, FAPAR (Fraction of AbsorbedPhotosynthetically Active Radiation) and FCOVER (Fraction of Cover) products from SPOT-4, SPOT-5, LANDSAT-7 and LANDSAT-8 images, using a water cloud model to obtain soil moisture estimations over grassland areas. Despite the considerable contributions of optical data to the estimation of biophysical vegetation parameters, due to saturation effects, their application to the case of high or very dense vegetation (with high values of LAI) is often inadequate. Secondly, in the case of regions with a high average level of cloud cover, such as tropical zones, high quality optical acquisitions are very limited and/or infrequently recorded. In this context, several studies have also assessed the sensitivity of SAR signals at different radar wavelengths (mainly the L, C and X-bands) to vegetation conditions [49; 50; 51; 52]. For example, [PERSON] et al. [52] revealed the high sensitivity of TSX and CSK data to increasing LAI in the case of cereal cover (\(\sim\)0.6 dB/1 m\({}^{2}\)/m\({}^{2}\)), with a coefficient of determination greater than 0.70. [PERSON] et al. [44] also reported the high sensitivity of X-band data to LAI, when the soil moisture lies between 10 and 20 vol. % over grassland areas. [PERSON] et al. [51] analyzed the potential interpretation of TSX data as a function of sugarcane height, showing that the backscattering coefficient increases with crop height at a high incidence angle (47\({}^{\circ}\)). The Sentinel-1 constellation was launched in 2014 (Sentinel-1A) and 2016 (Sentinel-1B), with both satellites carrying a C-band radar payload. Thanks to its short temporal repeat frequency (six days), as well as the high spatial resolution (10 m) provided by its payload, this constellation is making it possible to achieve regular operational estimations of the conditions and characteristics of the continental surface. In this context, the main objective of the present study is to make use of a large database to analyze the potential of Sentinel-1 (S1) data for the assessment of different soil and cereal parameters. The synergetic use of S1 radar data with Landsat high-resolution optical data is also analyzed. Section 2 describes the study site, satellite images and ground measurement database.