## 4 Results
### Assimilation of SSM In Situ Measurements  
In this section, the retrieval of irrigation using in situ SSM measurements with a revisit time mimicking potential satellite overpasses of 3-, 6- and 12-day windows are presented. Figure 3 displays the time series of observed, analyzed and open-loop (without data assimilation meaning no irrigation water inputs are applied) SSM together with measured and retrieved irrigations with a 6-day revisit time for the irrigated and the rainfed fields, respectively. Figure 12 in the Appendix A is the same as Figure 3 but for the rainfed field. In addition, Figure 4 shows the statistical metrics as a function of the revisit times of 3, 6 and 12 days. The open-loop (with no irrigation inputs) obviously failed in reproducing the SSM observations while the analyzed SSM (corrected by assimilating SSM observations) are much closer to the observations. In terms of irrigation retrieval, among 20 observed events considering the three fields, 17 irrigation events were reasonably detected within a delay ranging from 1 to 5 days. For instance, on field F2, the eight observed irrigation events are detected within 0 to 6 days around the real time of application and among them 6 within 0 to 2 days. Likewise, annual amounts are in good agreement with the actual events for F1 and F2 (Figure 5) with an average absolute difference of 3.5 mm (5.8%) and 5.8 mm (9.2%) over F1 and F2, respectively, while it is about 44% on F3. On this field (F3), irrigation practice is very specific as already underlined and the amounts are estimated by subtracting the previous metering to the observed value registered by the flowmeter at the day of survey. This means that the timing of irrigation as well as the amount are very uncertain if surveys are not carried out on time. Finally, the seasonal distribution of events is also in agreement with irrigation practices in the region as no event was detected during the senescence period when the soil moisture was low. By contrast, some false detections are observed (end of February on F1; start of the season on F3) and some events were missed by the retrieval algorithm. For the latters, a finer analysis pointed out the important role of rainfall that drastically impacts the irrigation retrieval when irrigation input is concomitant with a rainfall event. Indeed, rainfall can refill the thin superficial layer such as no irrigation input is necessary to match the SSM observation. On F1 for instance, the third event is not detected as two rainfall events are registered around the irrigation for a total amount of 35 mm. A similar remark can be drawn for the undetected event observed at the end of March on F2. On F3, at the beginning of the growing season (see the shaded area on Figure 3), three events are detected while no observation was available during this period because the flowmeter was installed on January 21 st. Nevertheless, large irrigation events at this time of the season are in-line with the farmers practices that water the fields in order to favor emergence and to avoid soil crusting. For the drip irrigated fields D1 and D2, the seasonal timing of the detected events is also in-line with the agricultural practices of the region as there is no retrieved event during senescence. This is particularly prominent on D1, in particular during 2017-2018 when the farmer stopped irrigation as early as February. By contrast, some false detections are observed on D2 at the end of the two seasons. The detected events are consistent with the SSM values close to field capacity during the first season at this time. These high values of SSM were related to the very specific growing conditions on this field with dense adventies and the wheat stems that were laid down by high winds from mid-April. This probably created a shielding layer that limited soil evaporation. The false detection at the end of the second season could also be related to the error on the SSM observations leading to a spurious slight increase between two observations.