Record link: 
http://hdl.handle.net/20.500.12708/83917
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Title: 
Estimation of high-resolution soil moisture using machine learning, satellite observations and ground measurements. A case study in a hilly agricultural region
en
Citation: 
Zappa, L., Forkel, M., Xaver, A., & Dorigo, W. (2020). Estimation of high-resolution soil moisture using machine learning, satellite observations and ground measurements. A case study in a hilly agricultural region. In EGU General Assembly 2020. EGU General Assembly 2020, Vienna, Austria. Copernicus. https://doi.org/10.5194/egusphere-egu2020-8338
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Publisher DOI: 
10.5194/egusphere-egu2020-8338
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Publication Type: 
Konferenzbeitrag - Abstract Book Contribution
de
 
Inproceedings - Abstract Book Contribution
en
Language: 
English
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Authors: 
Zappa, Luca 
Forkel, Matthias 
Xaver, Angelika 
Dorigo, Wouter 
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Organisational Unit: 
E120-01-2 - Forschungsgruppe Klima- und Umweltfernerkundung 
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Published in: 
EGU General Assembly 2020 
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Date (published): 
2020
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Event name: 
EGU General Assembly 2020 
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Event date: 
4-May-2020 - 8-May-2020
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Event place: 
Vienna
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Event place: 
AT
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Number of Pages: 
1
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Publisher: 
Copernicus
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Publisher: 
Copernicus GmbH
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Science Branch: 
Geodäsie, Vermessungswesen
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Appears in Collections:
Conference Paper

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