## 1 Introduction  
Crop monitoring by satellite remote sensing requires high spatial and temporal resolution image time series and ground campaigns to monitor the entire crop cycle with frequent ground acquisitions over extensive areas. With the ever-increasing number of satellites and the availability of free data, the integration of multisensor images in coherent time series offers new opportunities for land cover and crop type classification [1]. In addition, satellites with a shorter revisit time (e.g., six days at the Equator for the Sentinel-1 constellation and five days at the Equator under cloud-free conditions for the Sentinel-2 constellation) and reconfigurable acquisitions (different viewing conditions can be applied for more frequent observation of a certain area) can be used to better identify the different growth cycle stages that are often imperceptible when using more sporadic data. Many studies have combined optical and microwave images to improve mapping accuracy in agricultural scenarios [2; 3; 4; 5; 6; 7]. SAR data are independent of solar illumination and depend on the wavelength and on the roughness, geometry and material contents of the targeted surface. In contrast, optical data are greatly influenced by cloud cover and represent the reflectance of solar energy from a target area. The potential of Earth Observation (EO) techniques for the management of land and water resources has also been widely acknowledged [8; 9]. The repeatability of observations on a cyclic basis and the availability of high spatial resolution multispectral data are particularly suitable for cost-effectively mapping crops and irrigated areas with satisfactory accuracy [10]. The amount of water required to meet the cropped field's water loss through evapotranspiration is defined as the crop water requirement [11; 12]. Water requirements vary from crop to crop and throughout the growing season of an individual crop. The FAO Penman-Monteith method is the standard method for the definition and computation of the reference evapotranspiration (\(ET_{o}\)) [13; 14]. Crop evapotranspiration (\(ET_{c}\)) from crop surfaces under standard conditions is determined by crop coefficients (\(K_{c}\)) that relate \(ET_{c}\) to \(ET_{o}\). The dual crop coefficient approach separates \(K_{c}\) into two separated coefficients, one for crop transpiration (\(K_{\rm cb}\), basal crop coefficient) and another for soil evaporation (\(K_{\rm e}\)). EO methodologies have been used to estimate \(ET_{c}\) due to the reflective properties of vegetation and to the relationship of \(ET_{c}\) with crop characteristics, such as the Leaf Area Index (LAI) and crop coefficient (\(K_{c}\)). \(ET_{c}\) can be estimated from EO data using physics-based methods based on the surface energy balance [15; 16; 17] or using empirical methods based on the use of vegetation indices [18; 19; 20; 10]. Physics-based methods estimate latent heat flow through the surface energy balance, but the difficulties related to the measurement of its terms have led to a wider use of the empirical methods, in which the crop coefficient is obtained through the vegetation indices approach. The estimation of \(ET_{c}\) based on vegetation indices, usually the NDVI, is a modification of the crop coefficients method [11; 12], in which \(ET_{o}\) is calculated based on meteorological data, and the crop coefficient introduces information related to the crop. In these methods, crop data are often obtained through tabulated values, which provide general data for several crops [13; 14]. To improve the accuracy of crop water requirement estimation, the characterization of the \(K_{c}\) curves must be improved and can be done using EO data, because crop characteristics are well correlated with the spectral reflectances [21]. Thus, as proposed by several authors [10; 18; 19; 20], the \(K_{c}\)-NDVI approach establishes an empirical relationship between \(K_{c}\) values obtained through field measurements and NDVI values retrieved from EO optical data.