# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation  
[PERSON]  
1 CESBIO, CNES/CNRS/INRAE/IRD/UPS, Universite de Toulouse, 18 Av. [PERSON], Bpi 2801, CEDEX 9, 31401 Toulouse, France; [EMAIL_ADDRESS] (N.J.); [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (N.R.-F.); [EMAIL_ADDRESS] (R.M.) [PERSON]  
1 CESBIO, CNES/CNRS/INRAE/IRD/UPS, Universite de Toulouse, 18 Av. [PERSON], Bpi 2801, CEDEX 9, 31401 Toulouse, France; [EMAIL_ADDRESS] (N.J.); [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (N.R.-F.); [EMAIL_ADDRESS] (R.M.) [PERSON]  
1 CESBIO, CNES/CNRS/INRAE/IRD/UPS, Universite de Toulouse, 18 Av. [PERSON], Bpi 2801, CEDEX 9, 31401 Toulouse, France; [EMAIL_ADDRESS] (N.J.); [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (N.R.-F.); [EMAIL_ADDRESS] (R.M.) [PERSON]  
3 CIRAD, CNRS, INRAE, TETIS, University of Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France; nicolas.baghdadi@tele  
a scatterometer, particularly data acquired by the ASCAT/METOP satellite series [13]. The European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture project also provides long time series by merging soil moisture estimations from active and passive sensors [14]. For soil moisture estimation at high spatial resolution, we identify products with an average resolution at approximately 1 km or at the plot scale [15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. There have been various studies that have developed methodologies based on low-resolution data disaggregation techniques, notably with measurements acquired in thermal infrared (MODIS) [25] or, more recently, data acquired by SAR sensors. The Synthetic Aperture Radar (SAR) technique offers a high spatial resolution estimate of the radar signal adapted to applications at agricultural field scale. The measured signal is dependent on the radar configurations (frequency, incidence angle, and polarization) and the dielectric and geometric properties of the surface. After numerous demonstration space missions (ERS, ASAR/ENVISAT, RADARSAT, etc. ), the arrival of Sentinel-1 constellation [26] in the context of the Copernicus program has enabled exponential growth in the use of these signals for monitoring soil moisture and the dynamics of the vegetation cover. Other soil moisture products are then offered only based on Sentinel-1 data, with three types of methodologies: one based on the direct inversion of physical or semiempirical models [27; 28; 29]; one based on the application of machine learning approaches and particularly neural networks [30; 31; 32]; and one based on the change detection technique [33; 34; 35]. For example, at plot scale, [PERSON] et al. [31] presented an Artificial Neural Network (ANN) approach with training using the coupling of the Integral Equation Model (IEM) and the Water Cloud Model (WCM) to provide an estimate of soil moisture at the scale of the agricultural plot. [PERSON] et al. [35] also proposed an approach at the plot scale with greater consideration of the vegetation cover and its effect on the temporal variation of the radar signal. [PERSON] et al. [33] proposed a change detection approach very close to the initial approach proposed with data from ASCAT scatterers [13] at a 1 km scale. For these products, which are highly useful for regional hydrology, the validation of existing products, despite the very interesting potential, still shows some limitations in different contexts, in particular that of dense vegetation covers but also in relatively complex contexts with strong heterogeneities in terms of land use and topography [36]. In this context, this study proposes to test hybrid approaches to soil moisture retrieval at a 1 km scale with the objective of improving the estimation accuracy of soil moisture. The approaches consider hybrid methodologies with a combination of a change detection approach with empirical modeling or machine learning. Section 2 presents in the first subsection the database used in this study in terms of soil moisture data and satellite measurements.