## 5 Discussion  
Even though some limits are highlighted in relation to the quality of the SSM products, the reasonable statistics obtained by assimilating the S1 products, close to those obtained by using in situ SSM demonstrates the feasibility of irrigation retrieval using high-resolution soil moisture products derived from S1 when compared to other studies. For example, in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TruPosRat (4 Days)** & **TruPosRat (5 Days)** & **IrrigEvntRat** & & **Pbias** \\ \hline F4 & F5 & F4 & F5 & F4 & F5 & F4 & F5 \\ \hline
0.50 & 0.44 & 0.63 & 0.56 & 1.15 & 1.09 & \(-\)3.14 & 3.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical metrics of the estimated irrigation over the flood irrigated F2 and F3 when assimilating S1-derived SSM for a 12-day window. Figure 7: (a) Retrieved irrigation versus observed irrigation accumulated by 15 days, over the drip-irrigated field D1 during both seasons 2016-2017 and 2017-2018 for a 6-day window when assimilating Sentinel-1 SSM products; (b) Retrieved seasonal sum of irrigation versus observed seasonal sum of all the studied fields in this section (D1 and R1 both seasons, F2 and F3). the study by [PERSON] al. [32], the seasonal dynamic of irrigation is well reproduced but the weekly and seasonal amounts retrievals suffered from significant biases. As already shown by other authors [32; 35], the retrieval of the exact timing and the amount of irrigation events is difficult for drip irrigation. Our statistics are close to those of [PERSON] et al. [29] who obtained a Pearson correlation coefficient of 0.69 on average for 5-day cumulative amounts. The seasonal amounts statistics also compared well with the results obtained using in situ measurements and using a thermal-based approach in [35]. Interestingly enough, the good seasonal statistics are sometimes obtained because some missed events are compensated by some false detections at the seasonal scale as already shown by [35]. One important point highlighted by this study is the importance of the revisit time as the best retrieval performance are obtained for a 3-day revisit time. The performance decreases for higher revisit time because the surface layer can dry out within a few days, in particular for the semi-arid climate of our study sites. This is in line with the work of [32] who showed that, on average, 81% of the observed events are detected with a revisit time ranging from 1.5 to 4 days while only 58% of the irrigation events were correctly detected with the 6-day revisit of combined S1A and S1B on 3 plots in Montpellier by [37]. [PERSON] et al. [38] reported that the detection of irrigation events can be difficult when it occurs 5-6 days before S1 acquisition for the same reason of quick soil drying. Likewise, [PERSON] et al. [32] encountered the same difficulty in detecting all events, especially in some periods where the revisit time of SSM observations goes above 8 days in-line with the results obtained in this study considering a frequency of observations of 12 days. These results highlights that a satellite constellation with a 3-day revisit time would be relevant for irrigation detection at the field scale, as will be provided in a near future by the Copernicus constellation after the launch of Sentinel-1C planned in 2022 [84]. This work also showed that irrigation events occurring concomitantly with rainfall may be missed, consistent with the work of [20; 30; 38]. Indeed, the superficial layer may be refilled totally with rainfall only. In that case, the irrigation amount cannot be \"seen\" by the superficial reservoir information alone, provided by SSM observations. Finally, the question arises of the genericity of the proposed approach applied here on wheat crops only.