Crop Water Availability Map** in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling †
Abstract
:1. Introduction
2. Methods
2.1. Satellite Data Pre-Processing
2.2. Crop Type Classification
2.3. Crop Growth and Water Demand Modelling with PROMET
3. Results
3.1. Simulation Results at Field Scale
3.2. Simulation Results on a National Scale for Austria
4. Conclusions and Outlook
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Migdall, S.; Dotzler, S.; Gleisberg, E.; Appel, F.; Muerth, M.; Bach, H.; Weikmann, G.; Paris, C.; Marinelli, D.; Bruzzone, L. Crop Water Availability Map** in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling. Eng. Proc. 2021, 9, 42. https://doi.org/10.3390/engproc2021009042
Migdall S, Dotzler S, Gleisberg E, Appel F, Muerth M, Bach H, Weikmann G, Paris C, Marinelli D, Bruzzone L. Crop Water Availability Map** in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling. Engineering Proceedings. 2021; 9(1):42. https://doi.org/10.3390/engproc2021009042
Chicago/Turabian StyleMigdall, Silke, Sandra Dotzler, Eva Gleisberg, Florian Appel, Markus Muerth, Heike Bach, Giulio Weikmann, Claudia Paris, Daniele Marinelli, and Lorenzo Bruzzone. 2021. "Crop Water Availability Map** in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling" Engineering Proceedings 9, no. 1: 42. https://doi.org/10.3390/engproc2021009042