## 1 Introduction  
During the last decade, many SAR missions (SENTINEL-1A/B, SAOCOM, ICEYE, DENALL, SEOSAR...) have been added to the well-known pioneer missions (ERS, Radarsat-2, RCM, Envisat, TerraSAR-X, Tandem-X, COSMO-SkyMed, Kompsat-5...). Consequently, access to a wide range of images acquired with different configurations has been facilitated, which reinforces the need to develop radar backscatter models capable of simulating the delivered signals in order to estimate the biophysical parameters of the surface. The analysis and interpretation of SAR signals remain challenging and can lead to conflicting explanations for our observations of the environment. It is recognized that backscatter coefficients are sensitive to multiple changes in surface conditions and that the same value may be the result of extremely different vegetation or soil conditions due to compensation and the equifinality effects of surface and radar antenna parameters. For example, a dry bare soil could have the same backscatter coefficient value as a well-developed wheat field depending on surface conditions (soil moisture, vegetation water content, roughness, plant density...) or antenna geometry (frequency, incidence angle, polarization...) [1; 2]. To better understand these effects of equifinality and to better differentiate the contributions related to the variation in surface states, numerous studies have treated these two entities separately [3; 4]. In periods of bare soil, surface soil moisture, roughness and texture have been identified as variables whose dynamics influence the intensity of backscattering [5; 6; 7; 8; 9]. These three surface descriptors thus constitute explanatory variables common to many approaches, with the models being based on relationships determined from in situ datasets (semi-empirical models [10; 11; 12]) or on a physical description of backscatter processes (mechanistic models [13; 14; 15; 16; 17; 18]). In remote sensing, most models used for thematic applications are semi-empirical or statistical because of their ease of application for large landscapes. Physical models are currently used for theoretical surface conditions [19; 20; 21; 22; 23], where the required soil characterization or computation time is not feasible at the scale of a territory covered by a satellite image. They are fundamental to better understanding the different contributors of the backscattered signal but remain limited to specific cases (a very well-known plot or a simulated theoretical plot, for example), where scientists have total control over the surface conditions. With the objective of applying the method over large agricultural surfaces, we will not consider these physical models and we will concentrate on the most used models in applied remote sensing. Approaches based on empirical, semi-empirical or statistical models have varying levels of complexity and are based on a different number of surface descriptors. Nevertheless, these modeling approaches offer limited performance regardless of the tested sensor configurations, particularly for applications aimed at monitoring agricultural surfaces [24]. In this context, more recent studies have proposed different ways to modify models (i.e., by recalibration, taking into account biases or even adding empirical relationships to physical models) and attempted to improve the description of backscatter processes by taking better account of surface variables [24; 25; 26; 27; 28; 29; 30]. As models often present limitations in the representation of surface roughness, modifications were proposed that aimed at improving the formalism and extending the field of validity [24] in order to make it possible to apply the approaches to the high values observed in the agricultural context [31; 32; 33]. These modeling efforts have obviously been accompanied by a reduction in dispersion and bias, resulting in an overall increase in performance.