Water resources management has become a challenging problem worldwide, especially in developing countries. The lack of information on land cover monitoring has a huge impact on the water resources management, since it may hamper the collection, treatment and distribution of water for human consumption and agricultural development. To fulfil this demand, Earth Observation (EO) data has been used in African countries to map land cover of large and often inaccessible areas, providing useful information on qualitative and quantitative land cover changes. Although optical data methods for land cover classification are well established and almost operational, these data are not applicable to regions where the cloud coverage is frequent. In these regions, the use of Synthetic Aperture Radar (SAR) data are an alternative due to its ability to acquire data regardless of weather conditions and day/night cycle. The aim of this study is to assess the complementarity and interoperability of optical and polarimetric SAR data to map and monitor land cover of an agricultural area in Angola. For this purpose, SPOT-5 Take-5 images, Sentinel-1 dual polarisation (VV+VH) images and field data acquired during the 2015 growing season are used. SPOT-5 Take-5 experiment images are used, as a proxy of Sentinel-2 data, to evaluate the potential of its enhanced temporal resolution for agriculture applications. The field data collection and classification focused on the main crops grown in the region, which include: maize, soybean, bean and pasture. Independently of the crop type, and of the acquisition date, is clear that the overall classification accuracy and Kappa index are highest when both polarizations are considered together with the optical bands. Normalized Difference Vegetation Index (NDVI) and VV and VH polarization backscattering time series were used to compute the basal crop coefficients (Kcb) curve for each crop and to estimate the length of each phenological growth stage. A significant correlation between VV and VH bands and NDVI was observed for all classes, showing the consistency of both optical and microwave time series, proving that optical data can be replaced by microwave in the presence of a cloud cover. Basal crop coefficients were then used to compute the crop evapotranspiration and subsequently to estimate the crop irrigation requirements based on a soil water balance model. The identification of all the stages within the crop's cycle (initial, crop development, mid-season and late-season) was not possible since once only less than half crop cycle was available. Therefore, to a proper characterization of the crops phenology a high temporal resolution time series covering the entire growing season is required. The study was developed in the scope of the ESA Alcantara initiative project (Ref: 14-P13) and SPOT-5 Take-5 project ID: 29142.