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

Silke Migdall; Sandra Dotzler; Eva Gleisberg; Florian Appel; Markus Muerth; Heike Bach; Giulio Weikmann; Claudia Paris; Daniele Marinelli; Lorenzo Bruzzone

doi:10.3390/engproc2021009042

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

Silke Migdall^*, Sandra Dotzler, Eva Gleisberg, Florian Appel, Markus Muerth, Heike Bach, Giulio Weikmann, Claudia Paris, Daniele Marinelli, Lorenzo Bruzzone

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

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

Original language	English
Article number	42
Journal	Engineering proceedings
Volume	9
Issue number	1
DOIs	https://doi.org/10.3390/engproc2021009042
Publication status	Published - 24 Jan 2022
Externally published	Yes

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10.3390/engproc2021009042Licence: CC BY

Migdall-2022-Crop-water-availability-mapping-in-Final published version, 1.14 MBLicence: CC BY

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

abstract = "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",

author = "Silke Migdall and Sandra Dotzler and Eva Gleisberg and Florian Appel and Markus Muerth and Heike Bach and Giulio Weikmann and Claudia Paris and Daniele Marinelli and Lorenzo Bruzzone",

note = "Funding Information: This research was funded by the European Union{\textquoteright}s Horizon 2020 research and innovation program under grant agreement No. 825258. Publisher Copyright: {\textcopyright} 2022 by the authors.",

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doi = "10.3390/engproc2021009042",

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T1 - Crop Water Availability Mapping in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling

AU - Migdall, Silke

AU - Dotzler, Sandra

AU - Gleisberg, Eva

AU - Appel, Florian

AU - Muerth, Markus

AU - Bach, Heike

AU - Weikmann, Giulio

AU - Paris, Claudia

AU - Marinelli, Daniele

AU - Bruzzone, Lorenzo

PY - 2022/1/24

Y1 - 2022/1/24

N2 - 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

AB - 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

UR - https://iris.unitn.it/handle/11572/330200

U2 - 10.3390/engproc2021009042

DO - 10.3390/engproc2021009042

M3 - Article

VL - 9

JO - Engineering proceedings

JF - Engineering proceedings

SN - 2673-4591

IS - 1

M1 - 42

ER -

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

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