## 5 Discussion
The method relies on three main assumptions that are discussed: (1) with regards to the work of [38] relying on relative change between observed and predicted SSM, absolute values of SSM are used in this study meaning that the soil hydraulic characteristics must be known as they are needed to determine the soil parameters of the FAO-56 approach (see Appendix A); (2) the irrigation technique must be prescribed; (3) (not specific to our approach) soil moisture products and rainfall observations must be of \"good\" quality. Calibrating the FAO-56 model through information on soil characteristics is a potential drawback of the approach as soil are not always known with a good accuracy at the field scale on large area. Global maps of soil [85] could be used but some inaccuracies have been highlighted [86]. A promising approach to overcome this limitation is the histogram matching. It has been extensively used for the assimilation of coarse scale SSM products derived from radiometer in land surface models since the pioneering work of [PERSON] et al. [87], but it needs a relatively long historical records that will be provided by Sentinel-1 in a few years. The prescription of the irrigation techniques in this approach is needed mainly to determine the range of irrigation amount per event and the time constraint between two events for the flooding technique. This is a drawback as the map of irrigation techniques used by the farmer at field scale are not available apart from some areas where public policies are implemented for fostering conversion to water saving techniques such as drip. Determining the technique of irrigation from remote sensing is still an open question [88] and this drawback is thus common to several past works. [PERSON] et al. [38] for instance forced constant amounts computed from historical observations which means that irrigation surveys are needed for a long period of time. In addition, the rainfall data and SSM products must obviously be accurate. SSM products used in this work suffered from strong outliers in one field characterized by very specific growing conditions [17]. In addition, they were developed for wheat because it is the main crop in the whole southern Mediterranean region. The main limitation of this proposed retrieval approach for application to other crops is that the radiative transfer model within the inversion process has been calibrated for wheat. Radar being very sensitive to the plant geometry, the algorithm could probably provide with acceptable results on crop with a similar geometry such as barley. By contrast, the soil moisture retrieval approach would likely require a new calibration of the radiative transfer model on more complex crops (row crops) with different geometry (sunflower with a planophile leaf orientation distribution for instance). For more complex crops, either the method of [PERSON] et al. [17] should be adapted (our work in progress is to evaluate the approach on maize), either other SSM products could be used (for instance those of [PERSON] et al. [39]). Finally, SSM products may all suffer from uncertainties in specific conditions. Dry sandy soils for instance are well known to be characterized by high backscattering coefficients [89] due to volume diffusion [90] and soil moisture retrieval methods based on simple soil backscattering models such as the one used in this study often fail within this particular conditions making the irrigation retrieval difficult.