## 1 Introduction
On average, microwave data including SAR and observations from passive radiometers data have shown high potentialities for irrigation mapping with or without combining with optical data. By contrast, only a few studies aimed to retrieve irrigation water timing and amounts based on remote sensing observations. Among them, the pioneering work of [PERSON] et al. [20] aimed to estimate monthly irrigation amounts over four different areas (Morocco, USA, Australia and Spain) using SSM from coarse resolution satellites including SMOS, SMAP, ASCAT and AMSR-2 (\(\sim\)40 km) and the adaptation of the SM2 RAIN algorithm [28] that was originally developed for rainfall estimation. The approach yielded a good estimate when the error on SSM is low and the revisit time is high. In particular, the results of a synthetic experiment of the approach show that its performance on semi-arid sites is limited for errors higher than 0.03 m\({}^{3}\)/m\({}^{3}\) and revisit time higher than 3 days. This work was recently declined by [29] in Spain using downscaled soil moisture estimate at 1 km resolution and by [PERSON] et al. [30] in Iran. These studies revealed the significant loss of performance in the presence of significant and regular precipitation during the irrigation period making difficult the irrigation detection for winter crops. [PERSON] et al. [11] used in situ hourly observations to further analyze the quality of the irrigation retrieval as a function of the revisit time of the products: their results showed that a significant loss of performance could be expected if the temporal resolution is decreased from one hour to 36 h (correlation coefficient R decreasing from 0.88 to 0.6) while R falls to 0.1 for 120 h (5 days \(\sim\) S1 overpass). A theoretical study aiming to quantify irrigation amounts based on the assimilation of synthetic SSM observations into a land surface model was published recently by [31]. [PERSON] et al. [21] also used a water balance model and coarse scale SSM products to derive irrigation amounts from the analysis increments. Recently, [PERSON] et al. [32] developed an algorithm to retrieve the timing and the amount of irrigation in near real-time between two S1 acquisitions in Germany using 500 m resolution SSM products derived from Copernicus products at 1 km resolution [33].Beyond these studies based on coarse scale products, some attempts to retrieve irrigation at higher resolution have also been carried out at the scale of the field in many agricultural area including the Mediterranean region [20; 21; 22; 34] where the field size ranges between 1 and 5 ha [17; 35; 36]. [PERSON] et al. [37] developed an approach for the retrieval of irrigation timing in near real-time using S1 backscattering coefficient and S2 NDVI over different crop fields in France and Spain. The approach is based on a change detection approach between two successive S1 observations to detect irrigation. [PERSON] et al. [38] also developed a method to detect the timing of irrigation only over maize fields in France. To this objective, the authors compared the change of SSM products derived from a synergy of S1 and S2 [39] with the change of SSM predicted by the FAO-56 model [40] without irrigation forcing between two SSM observations. To our knowledge, the only study aiming to retrieve both timing and amounts at the field scale using high resolution remote sensing data was published recently by [35]. These authors used thermal-infrared data acquired by Landsat and showed promising results for several fields in Morocco. Nevertheless, thermal and visible observations are prone to atmospheric observations that can seriously hamper the application of the approach in cloudy regions, especially when using thermal sensors that currently have a relatively low temporal resolution (repeat cycle of 16 days for Landsat). Within this context, the objective of this work is to propose and to evaluate a new approach to retrieve irrigation amounts and timing at the field scale using S1 data.