Multi-year mapping of water demand at crop level:An end-to-end workflow based on high-resolution crop type maps and meteorological data

This article presents a novel system that produces multiyear 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 multiyear crop type maps, which are assimilated together with the Sentinel-2 dense time series and the meteorological data into a physically based agrohydrological model to derive the irrigation water demand for different crops. To process the required large volume of multiyear Copernicus Sentinel-2 data, the software architecture of the proposed system has been built on the integration of the Food Security thematic exploitation platform (TEP) and the data-intensive artificial 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

Crop Water Availability Mapping in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling

The Danube Basin has been hit by several droughts in the last few years. As climate change makes weather extremes and temperature records in late winter and early spring more likely, water availability and irrigation possibilities become more important. In this paper, the crop water demand at field and national scale within the Danube Basin is presented using a dense time series of multispectral Sentinel-2 data, for crop type maps derived with deep learning techniques and physically-based models for crop parameter retrieval and crop growth modelling