# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 1 Introduction  
Soil properties play a key role in various processes related to continental surface hydrology, describing the distribution of rainwater or irrigation between runoff, seepage, and evapotranspiration [1; 2; 3]. In this context, although ground measurements have long been used to estimate the water status or soil texture, they are time-consuming and costly procedures, and are generally considered to be inadequate, since they do not allow spatio-temporal variations in soil moisture to be correctly monitored. The scientific community has thus devoted considerable effort to the development of remote sensing products, in an effort to improve the accuracy and spatio-temporal coverage of these observations when they are interpreted for hydrological applications and water resource management [4; 5; 6; 7; 8]. In recent years, remote sensing has been used to retrieve soil properties such as soil moisture, roughness, soil texture. Among these approaches, promising techniques such as spectroscopy in the visible (VIS) and near-infrared (NIR) spectroscopy have been used as proximal sensing techniques and enable the measurement of texture. Indeed, soil components are sensitive to these portions of the electromagnetic spectrum, which allow the assessment of soil textural composition [9]. [PERSON] et al. [10] have predicted soil texture (sand, clay, and silt) using VNIR-SWIR band reflectance spectroscopy. High resulting accuracies were found for both of clay, sand silt fraction, using the partial least-squaresregression (PLSR) methods with coefficients of determination of 0.87, 0.8 and 0.6, respectively. [11] used the VIS-NIR spectroscopy to predict and map sand and clay fraction over agricultural fields with a regression coefficient of 0.9 and 0.91, respectively. Since 2014, Sentinel-1 radar data has been available over Europe and other regions of the globe, with a 6-day repeat cycle and both high and fine spatial resolution (ranging between 5 and 40 m, depending on the acquisition mode and the product type). Various algorithms have been developed for the generation of operational soil moisture products, in particular for agricultural areas. The present authors have computed soil moisture products over agricultural fields with a vegetation cover, using a synergetic combination of optical and radar data, through the use of neural networks, a change detection and the simple inversion of a backscattering model [12; 13; 14; 15]. Soil texture is a key parameter in the development of PedroTransfer Functions (PTFs), which are based on soil properties (physical, chemical and biological), and are often used to estimate difficult-to-measure hydraulic soil properties. In particular, clay content has a strong influence on the hydraulic properties of soils, and can be interpreted in terms of the soil's water-holding capacity. Several studies have been proposed for the estimation of soil texture components, using remotely sensed optical images. These techniques rely mainly on optical data and the interpretation of ground reflectance to determine soil color [16; 17; 18]. More recently, several techniques have been developed for the interpretation of various types of optical satellite observation (Landsat, SPOT, Sentinel-2, etc.) [19; 20; 21; 22; 23; 24; 25]. These are restricted mainly to the analysis of bare soils, since the observation of fields with a vegetation cover can be affected by the noise produced by canopies. For instance, [PERSON] et al. [26] proposed a method for clay content estimations, based on the mid-infrared index (MID) derived from the TM 5 and TM 7 bands of Landsat data, recorded over central Tunisia. [PERSON] et al. [27] recently predicted several toposoil properties (clay, SOC, iron, pH, etc.) from a chemometric model, using Sentinel-2 spectral band observations. Their clay content predictions are correlated with in situ measurements, with a coefficient of determination (R\({}^{2}\)) equal to 0.39 and 0.42 for the Versailles Plain and the La Peyne Valley (France), respectively. [PERSON] et al.