## 7 Conclusions  
The paper reports a detailed analysis on the influence of main SVV on \(\sigma^{\circ}\). Statistical analyses have been carried out to define suitable frequencies/polarimetry configuration to characterize the main SVV over bare and vegetated plots. Dataset includes X, C and L SAR bands with HH, VV, VH and HV polarizations and in situ measurements.  
The dynamic and correlation analyses investigate the behavior of single bands with changing SVV. The inter-channel correlation analysis explores the behavior of band pairs with SVV changing over time.  
The dynamic analysis revealed that cross-polarized channels exhibit higher sensitiveness to SVV, in particular to _LAI_ and \(F_{\mathrm{v}}\).  
The single correlation analysis revealed that: (i) over bare soil, \(\sigma^{\circ}\) vs. \(m_{\mathrm{v}}\) exhibits strong and very strong correlation at all channels; best \(r\) characterizes \(\sigma^{\circ}\) vs. \(m_{\mathrm{v}}\) at L channels (\(r\) -1); (ii) over vegetated areas, highest \(r\) characterizes \(\sigma^{\circ}\) vs. \(F_{\mathrm{v}}\) and \(\sigma^{\circ}\) vs. _LAI_ with best suitable channels at L frequency.  
The inter-channel correlation (using a single frequency) displayed significant \(r_{\mathrm{s}}\) vs. \(h^{\prime}\) correlations over both bare and vegetated plots (unachievable by means of the single regression approach). The inter-channel spatial/temporal regression analysis is more suitable than the single channel approach in several cases. Thus, the inter-channel correlation analyses promise to retrieve other SVV with good accuracies even over vegetated plots. In some cases, best correlations are found by adopting the dual frequency approach compared to the use of a single frequency.  
Despite all the analyses confirming that \(L\) bands are more suitable than others to infer SVV over both bare and vegetated plots, few satellite sensors provide SAR data at L frequency (PALSAR 1 and 2); as a consequence, in the past, most of the models (see Table 1) have been implemented on C and X bands only (widely available). In addition, the NASA JPL's Soil Moisture Active Passive (SMAP), a mission launched at the end of 2015, partially failed after collecting just two months of data because of the malfunctioning of the SAR (L\({}_{\mathrm{VV}}\), L\({}_{\mathrm{HH}}\), and L\({}_{\mathrm{HV}}\)).  
Based on the results achieved by means of the inter-channel analysis, it could be interesting to perform further experiments exploiting data collected also by the near future SAR sensors; including: (i) SAOCOM-CONAE 1A/1B (Argentina) an L band two-satellite flying in constellation with COSMO-SkyMed; (ii) Tandem-L (DLR-Germany) a two formation-flying radar satellites operating in L band (planned to be launched at the end of 2022), and (iii) NISAR (NASA JPL-United States, 2020) that will include both L and S band polarimetry SAR sensors. These new experiments could be carried out coupling SAR to optical retrieved vegetation biomass (e.g., by means of Sentinel 2 or Landsat 8) and soil water content (e.g., through the use of passive microwave) maps. Thus, widening the study area these new researches could point out expected limitations in the relationship's inversion.  
The authors thank the German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt-DLR) for acquiring the radar images within the ESA-funded project AgriSAR 2006. A special thank both to the _Leibnitz-Zentrum fur Agarlandschaforschung_ (ZALF) and University of Kiel (_Christian-Albrechts-Universitat_, Department of Geography) for having carried out a consistent in situ dataset and the Instituto Nacional de Tecnica Aeroespacial of Torrejon de Ardoz of Madrid (INTA institute) for radiometric calibrating and atmospherically correcting the optical images.  
F.C. designed and performed the sensitivity analysis; [PERSON] and A.M. analyzed the results; all the authors wrote and approved the paper.  
The authors declare no conflicts of interest.