This paper presents a novel system that produces multi-year high-resolution irrigation water demand maps for agricultural areas enabling a new level of detail for irrigation support for farmers and agricultural stakeholders. The system is based on a scalable distributed Deep Learning (DL) model trained on dense time series of Sentinel-2 images and a large training set for the first year of observation, and fine-tuned on new labeled data for the consecutive years. The trained models are used to generate multi-year crop type maps, which are assimilated together with the Sentinel-2 dense time series and the meteorological data into a physically based agro-hydrological model to derive the irrigation water demand for different crops. To process the required large volume of multi-year Copernicus Sentinel-2 data, the software architecture of the proposed system has been built on the integration of the Food Security TEP and the data-intensive arpngicial intelligence Hopsworks platform. While the Food Security TEP provides easy access to Sentinel- 2 data and the possibility of developing processing algorithms directly in the cloud, the Hopsworks platform has been used to train DL algorithms in a distributed manner. The experimental analysis was carried out in the upper part of the Danube Basin for the years 2018, 2019 and 2020 considering 37 Sentinel-2 tiles acquired in Austria, Moravia, Hungary, Slovakia and Germany.