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

Giulio Weikmann, Daniele Marinelli, C. Paris, Silke Migdall, Eva Gleisberg, Florian Appel, Heike Bach, Jim Dowling, Lorenzo Bruzzone

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
53 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)6758-6775
Number of pages18
JournalIEEE Journal of selected topics in applied earth observations and remote sensing
Volume16
Early online date13 Jul 2023
DOIs
Publication statusPublished - 2023

Keywords

  • AI4Copernicus
  • Computational modeling
  • Copernicus
  • Crops
  • Deep Learning (DL)
  • Earth Observation
  • ExtremeEarth
  • Food security
  • Irrigation
  • Irrigation Water Demand
  • Monitoring
  • PROMET
  • Stress
  • Sustainable Food Production
  • Thematic Exploitation Platform
  • Water resources
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

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