Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling

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

Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling

Silke Migdall
, Sandra Dotzler
, Christian Miesgang
, Florian Appel
, Markus Muerth
, Heike Bach
, Giulio Weikmann
, Claudia Paris
, Daniele Marinelli
, Lorenzo Bruzzone

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

20 Downloads (Pure)

Abstract

Water is one of the most precious resources on this planet. With climate change, weather conditions, water availability and food security show ever higher variability. In this paper, the reaction of the different crop types in the Austrian part of the Danube basin to the extreme drought during 2018 in terms of water stress and water use efficiency are shown. For this, crop types were classified using deep learning methods and Sentinel-2 data were analyzed and combined with crop growth modelling to derive the water stress levels of the different crops.

Original language	English
Title of host publication	Proceedings of the 2021 conference on Big Data from Space
Publisher	Publications Office of the European Union
Pages	69-72
ISBN (Electronic)	978-92-76-37661-3
Publication status	Published - 1 Jan 2021
Externally published	Yes
Event	Conference on Big Data from Space, BiDS 2021: From Insights to Foresight - University POLITEHNICA of Bucharest, Bucharest, Romania Duration: 18 May 2021 → 20 May 2021 https://www.bigdatafromspace2021.org/

Conference

Conference	Conference on Big Data from Space, BiDS 2021
Abbreviated title	BIDS 2021
Country/Territory	Romania
City	Bucharest
Period	18/05/21 → 20/05/21
Internet address	https://www.bigdatafromspace2021.org/

Keywords

ITC-CV

Access to Document

ArticleFinal published version, 1.69 MBLicence: CC BY

https://iris.unitn.it/handle/11572/330202Licence: CC BY

Cite this

Migdall, S., Dotzler, S., Miesgang, C., Appel, F., Muerth, M., Bach, H., Weikmann, G., Paris, C., Marinelli, D., & Bruzzone, L. (2021). Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling. In Proceedings of the 2021 conference on Big Data from Space (pp. 69-72). Publications Office of the European Union. https://iris.unitn.it/handle/11572/330202

@inproceedings{bcbbfdcb4bdc457292d83129852d9537,

title = "Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling",

abstract = "Water is one of the most precious resources on this planet. With climate change, weather conditions, water availability and food security show ever higher variability. In this paper, the reaction of the different crop types in the Austrian part of the Danube basin to the extreme drought during 2018 in terms of water stress and water use efficiency are shown. For this, crop types were classified using deep learning methods and Sentinel-2 data were analyzed and combined with crop growth modelling to derive the water stress levels of the different crops.",

keywords = "ITC-CV",

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

year = "2021",

month = jan,

day = "1",

language = "English",

pages = "69--72",

booktitle = "Proceedings of the 2021 conference on Big Data from Space",

publisher = "Publications Office of the European Union",

address = "Luxembourg",

note = "Conference on Big Data from Space, BiDS 2021 : From Insights to Foresight, BIDS 2021 ; Conference date: 18-05-2021 Through 20-05-2021",

url = "https://www.bigdatafromspace2021.org/",

}

Migdall, S, Dotzler, S, Miesgang, C, Appel, F, Muerth, M, Bach, H, Weikmann, G, Paris, C, Marinelli, D & Bruzzone, L 2021, Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling. in Proceedings of the 2021 conference on Big Data from Space. Publications Office of the European Union, pp. 69-72, Conference on Big Data from Space, BiDS 2021, Bucharest, Romania, 18/05/21. <https://iris.unitn.it/handle/11572/330202>

Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling. / Migdall, Silke; Dotzler, Sandra; Miesgang, Christian et al.
Proceedings of the 2021 conference on Big Data from Space. Publications Office of the European Union, 2021. p. 69-72.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling

AU - Migdall, Silke

AU - Dotzler, Sandra

AU - Miesgang, Christian

AU - Appel, Florian

AU - Muerth, Markus

AU - Bach, Heike

AU - Weikmann, Giulio

AU - Paris, Claudia

AU - Marinelli, Daniele

AU - Bruzzone, Lorenzo

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Water is one of the most precious resources on this planet. With climate change, weather conditions, water availability and food security show ever higher variability. In this paper, the reaction of the different crop types in the Austrian part of the Danube basin to the extreme drought during 2018 in terms of water stress and water use efficiency are shown. For this, crop types were classified using deep learning methods and Sentinel-2 data were analyzed and combined with crop growth modelling to derive the water stress levels of the different crops.

AB - Water is one of the most precious resources on this planet. With climate change, weather conditions, water availability and food security show ever higher variability. In this paper, the reaction of the different crop types in the Austrian part of the Danube basin to the extreme drought during 2018 in terms of water stress and water use efficiency are shown. For this, crop types were classified using deep learning methods and Sentinel-2 data were analyzed and combined with crop growth modelling to derive the water stress levels of the different crops.

KW - ITC-CV

UR - https://doi.org/10.2760/125905

M3 - Conference contribution

SP - 69

EP - 72

BT - Proceedings of the 2021 conference on Big Data from Space

PB - Publications Office of the European Union

T2 - Conference on Big Data from Space, BiDS 2021

Y2 - 18 May 2021 through 20 May 2021

ER -

Water Stress Assessment in Austria based on Deep Learning and Crop Growth Modelling

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this