<div class="csl-bib-body">
<div class="csl-entry">Büechi, P. E., Dorigo, W. A., Reuß, F. D., Homolová, L., Pikl, M., Bartošová, L., & Chatzikyriakou, C. (2024, May 15). <i>Combining Sentinel-1 and 2 data with machine learning to improve field-scale crop yield forecasting</i> [Conference Presentation]. EO for Agriculture Under Pressure 2024 Workshop, Frascati, Italy. http://hdl.handle.net/20.500.12708/198038</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/198038
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dc.description.abstract
Climate change is threatening food security. To ensure food security, we do not only have to safeguard agricultural production but also optimally distribute the yields between regions. For that, decision-makers need reliable crop yield forecasts so that they can plan which regions are likely to experience crop yield losses and which regions will produce a surplus. Earth observation and machine learning are key tools to calculate such forecasts. Especially Sentinel-1 and 2 data has been used a lot as it provides regular high-resolution information about the state of crops and soil moisture. However, crop yield forecasts based on machine learning are strongly limited by the availability of field-level crop yield data, which farmers often do not like to share publicly. In this study, we evaluated if a model trained with data from a certain region can be applied elsewhere, to use training data more efficiently. Our field-level crop yield forecasts were trained using crop yield data from a farm (846 fields) in the Czech Republic for winter wheat. It was based on Sentinel-1 and 2 data and the machine learning model Extreme Gradient Boosting. The model was then tested for various farms with increasing geographical distance. The baseline was a forecast for fields of the same farm, that were not used for training. Next, the model was applied to another farm in Czechia, one in Ukraine and one in the Netherlands. The model transferability worked well for the other farm in Czechia (R²=0.64 between the forecast 1 month before harvest and observed yield). However, the model performed poorly further away than that (R²<0.13). This was related to very different weather conditions. Adding meteorological predictors or applying the model to more similar areas may help in the future to improve the transferability of the forecasts.
en
dc.language.iso
en
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dc.subject
Fernerkundung
en
dc.subject
Erntevorhersagen
en
dc.subject
Künstliche Intelligenz
en
dc.title
Combining Sentinel-1 and 2 data with machine learning to improve field-scale crop yield forecasting
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
Czech Academy of Sciences, Global Change Research Institute, Czechia
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dc.contributor.affiliation
Czech Academy of Sciences, Global Change Research Institute, Czechia
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dc.contributor.affiliation
Czech Academy of Sciences, Global Change Research Institute, Czechia
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dc.contributor.affiliation
Earth Observation Data Centre for Water Resources Monitoring (EODC), Austria
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dc.type.category
Conference Presentation
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tuw.researchTopic.id
E4
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tuw.researchTopic.name
Environmental Monitoring and Climate Adaptation
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E120-08 - Forschungsbereich Klima- und Umweltfernerkundung
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tuw.publication.orgunit
E120-01 - Forschungsbereich Fernerkundung
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tuw.author.orcid
0000-0001-8054-7572
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tuw.author.orcid
0000-0001-7455-2834
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tuw.event.name
EO for Agriculture Under Pressure 2024 Workshop
en
tuw.event.startdate
13-05-2024
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tuw.event.enddate
16-05-2024
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Frascati
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tuw.event.country
IT
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tuw.event.institution
ESA-ESRIN
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tuw.event.presenter
Büechi, Piet Emanuel
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wb.sciencebranch
Geodäsie, Vermessungswesen
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wb.sciencebranch
Informatik
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wb.sciencebranch
Physische Geographie
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wb.sciencebranch.oefos
2074
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1054
-
wb.sciencebranch.value
70
-
wb.sciencebranch.value
15
-
wb.sciencebranch.value
15
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item.languageiso639-1
en
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item.openairetype
conference paper not in proceedings
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item.openairecristype
http://purl.org/coar/resource_type/c_18cp
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item.grantfulltext
restricted
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item.cerifentitytype
Publications
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item.fulltext
no Fulltext
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crisitem.author.dept
E120-08 - Forschungsbereich Klima- und Umweltfernerkundung
-
crisitem.author.dept
E120-08 - Forschungsbereich Klima- und Umweltfernerkundung
-
crisitem.author.dept
E120-01 - Forschungsbereich Fernerkundung
-
crisitem.author.dept
Czech Academy of Sciences, Global Change Research Institute
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crisitem.author.dept
Czech Academy of Sciences, Global Change Research Institute
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crisitem.author.dept
Czech Academy of Sciences
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crisitem.author.dept
Earth Observation Data Centre for Water Resources Monitoring (EODC), Austria
