## 6 Conclusions and Perspectives  
A new approach for estimating irrigation amount and timing at the field scale by assimilating SSM in the FAO-56 using a particle filter algorithm is developed and evaluated over wheat fields located in the center of Morocco and irrigated with different techniques including drip and flood irrigation (in addition to non-irrigated fields). This method is based on Sentinel-1 data only and can avoid the use of optical data that are prone to atmospheric perturbations. In a first step, a synthetic experiment showed the need for additional information to the retrieval algorithm such as a time constraint for the flood irrigation technics when the revisit time of SSM is short (3 days). The approach is then evaluated using in situ measurements of SSM mimicking revisit times of 3, 6 and 12 days. Results show that the approach is able to accurately estimate the timing and amounts of irrigation over the irrigated fields while almost no irrigation is detected over the rainfed seasons. At 6 days, that mimics the S1 overpass, good scores are obtained but significant better statistics are obtained with a 3-day revisit time highlighting the added value of potential future missions providing a better time revisit than S1. The distribution of the events within the season is also well reproduced on average for the flood-irrigated fields where ~50% of the events are correctly detected using the only data set available before the launch of S1B suffering from a low revisit time of 12 days. By contrast, retrieval based on a 6-day revisit time using in situ observation resulted in more than 70% of event detected. Close results are finally obtained by using S1 SSM products and in situ measurements for the retrieval of 15-day cumulative amounts over the drip field D1: performance metrics are R = 0.64 (0.72) and RMSE = 28.7 mm (27.1 mm) using S1 products (in situ measurements). The assessment of the approach should be extended to other sites and climate conditions regarding the low number of data sets matching the period of Sentinel-1. Nevertheless, the databases providing with consistent irrigation water amounts at the field scale are very limited. Extending the validation fields could also allow analyzing the performance of the algorithm by growth stage as satellite SSM products are known to be of poor quality when the vegetation cover is dense. Even though reasonable results were obtained, some limitations related to the use of moisture of the upper soil layer are also highlighted. This points out the need for complementing the algorithm with information on the root-zone soil moisture corresponding to the real water available for the plant growth. Our work in progress is thus dealing with the joint assimilation of SSM and instantaneous estimate of ET derived from an energy budget model fed by remotely sensed surface temperature. Finally, the contrasted behavior between rainfed and irrigated fields open perspectives for the mapping of irrigated/rainfeed crops. Conceptualization, N.O. and L.J. ; methodology, N.O. and L.J. ; software, N.O. ; validation, N.O. and L.J. ; formal analysis, N.O. and L.J. ; investigation, N.O., L.J. and S.K. ; resources, L.J., S.K., O.M. and J.E. ; data curation, N.O. ; writing--original draft preparation, N.O. and L.J. ;writing--review and editing, N.O., L.J., O.M., M.L.P., J.E. and S.K. ; visualization, N.O., L.J. and S.K. ; supervision, L.J. ; project administration, L.J., O.M., J.E. and S.K. ; funding acquisition, L.J., O.M., J.E. and S.K. All authors have read and agreed to the published version of the manuscript. This work was conducted within the frame of the International Joint Laboratory TREMA ([[https://www.lmi-trema.ma/](https://www.lmi-trema.ma/)]([https://www.lmi-trema.ma/](https://www.lmi-trema.ma/)), accessed on 03 July 2021).