# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 1 Introduction  
Monitoring the surface soil moisture (SSM) in agricultural areas at the plot scale helps in many applications such as irrigation planning and crop management [1]. Over the last decade, SAR (Synthetic Aperture Radar) data have shown great potential in the estimation of SSM in agricultural areas [2; 3; 4; 5; 6; 7; 8; 9]. Numerous studies have assessed the potential of SAR data in the X and C bands for SSM estimates, whereas few studies have been conducted using the L-band due to the low availability of L-band data and the high availability of several SAR sensors operating in X and C bands [2; 7; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Currently, C-band data provided by Sentinel-1 (S1) two-satellite constellation offer a good opportunity for operational SSM mapping in agricultural areas because S1 provides free and open access data at a high spatial resolution (10 m \(\times\) 10 m) and a high revisit time (6 days over Europe) [21]. Several studies have developed operational methods that use S1 SAR data (C-band) to estimate SSM at the plot scale and high revisit times [9; 17; 22; 23; 24]. Methods developed by [PERSON] et al. [17] and [PERSON] et al. [22] use a neural network technique to invert the C-band signal and estimateSSM. These methods consist of using backscattering models to first generate synthetic database of SAR backscattering coefficients for wide ranges of soil conditions (soil moisture and roughness) and one vegetation parameter (for example NDVI, LAI, or vegetation water content). Then, train neural networks (NNs) for soil moisture estimation using the generated synthetic database. For the NNs training, the input vector is composed of SAR information (polarization and incidence angle) and one vegetation parameter (NDVI, LAI or vegetation water content) and the output vector contains the soil moisture only. The method for SSM estimation at high spatial resolution developed by [PERSON] et al. (2019) is a change detection approach that requires an SAR time series with a high revisit time. The change detection method supposes that the change in backscattering coefficients between two successive dates is only related to change in the soil moisture level while roughness and vegetation conditions is considered to remain stable. Even though those methods are operational, they may have limitations related to the penetration depth of SAR signals into the canopy cover. Indeed, in the case of low penetration depth, i.e., the emitted SAR wave cannot reach the underlying soil or the soil contribution to the backscattered wave is negligible, the SAR signal becomes insensitive to soil moisture and the estimated values of SSM are thus unreliable. The penetration depth into vegetation cover, as well as the behavior of SAR signals, depends on crop type and canopy growth stage ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2018). For instance, [PERSON] et al. (2017) reported that the penetration depth of the C-band SAR signals into wheat and grassland canopies becomes low when the NDVI (Normalized Difference Vegetation Index) surpasses 0.7 (no sensitivity to soil moisture). In contrast, for maize crop, [PERSON] et al. (2018) reported that the observed C-band backscattering response to soil moisture is notable even when the canopy is at peak biomass, which indicates that the C-band penetrates the well-developed maize canopy. Therefore, to better determine the possible limitations of the C-band for SSM estimates, it is important to assess the potential of the C-band to penetrate into the canopy cover. This can be realized by analyzing the temporal evolution of the C-band according to SSM variations and canopy development phases during a growth cycle ([PERSON] et al., 2018). The use of the L-band could overcome the limitations of the C-band for SSM estimates in the presence of well-developed canopy cover since the L-band (\(\sim\)24 cm) is characterized by a large penetration depth into the crop canopy ([PERSON] et al., 2018).