Leaf area index time series imputation for early yield prediction

Christoph Jörges; Jens E. d'Hondt; Georgios Chatzigeorgakidis; Silke Migdall; Christian Miesgang; Susanne Karg; Heike Bach; Panagiotis Betchavas; Dimitrios Skoutas

doi:10.2760/46796

Leaf area index time series imputation for early yield prediction

Christoph Jörges, Jens E. d'Hondt, Georgios Chatzigeorgakidis, Silke Migdall, Christian Miesgang, Susanne Karg, Heike Bach, Panagiotis Betchavas, Dimitrios Skoutas

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Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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Leaf Area Index (LAI) is a key parameter in crop growth models, and its accurate estimation is crucial for yield prediction. However, LAI data values are often missing or incomplete due to various reasons, such as sensor failures or cloud cover. In this paper, we propose a set of time series data imputation methods for LAI values derived from satellite images by radiative transfer model (RTM) inversion. The methods perform temporal interpolation either at the level of individual pixels or on spatial aggregates. Our experimental evaluation demonstrates that our approach can be applied to various crop types and has the potential to improve the accuracy and timeliness of yield prediction.

Originele taal-2	Engels
Titel	Proceedings of the 2023 conference on Big Data from Space (BIDS23)
Subtitel	From foresight to impact : 6-9 November 2023, Austrian Center, Vienna
Redacteuren	P. Soille, S. Lumnitz, S. Albani
Plaats van productie	Luxembourg
Uitgeverij	Office for Official Publications of the EC
Pagina's	373-376
Aantal pagina's	4
ISBN van elektronische versie	978-92-68-08696-4
DOI's	https://doi.org/10.2760/46796
Status	Gepubliceerd - 2 nov. 2023
Evenement	2023 Conference on Big Data from Space, BiDS’23 - Vienna, Oostenrijk Duur: 6 nov. 2023 → 9 nov. 2023

Congres

Congres	2023 Conference on Big Data from Space, BiDS’23
Verkorte titel	BiDS’23
Land/Regio	Oostenrijk
Stad	Vienna
Periode	6/11/23 → 9/11/23

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Jörges, C., d'Hondt, J. E., Chatzigeorgakidis, G., Migdall, S., Miesgang, C., Karg, S., Bach, H., Betchavas, P., & Skoutas, D. (2023). Leaf area index time series imputation for early yield prediction. In P. Soille, S. Lumnitz, & S. Albani (editors), Proceedings of the 2023 conference on Big Data from Space (BIDS23): From foresight to impact : 6-9 November 2023, Austrian Center, Vienna (blz. 373-376). Office for Official Publications of the EC. https://doi.org/10.2760/46796

Jörges, Christoph ; d'Hondt, Jens E. ; Chatzigeorgakidis, Georgios et al. / Leaf area index time series imputation for early yield prediction. Proceedings of the 2023 conference on Big Data from Space (BIDS23): From foresight to impact : 6-9 November 2023, Austrian Center, Vienna. redacteur / P. Soille ; S. Lumnitz ; S. Albani. Luxembourg : Office for Official Publications of the EC, 2023. blz. 373-376

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title = "Leaf area index time series imputation for early yield prediction",

abstract = "Leaf Area Index (LAI) is a key parameter in crop growth models, and its accurate estimation is crucial for yield prediction. However, LAI data values are often missing or incomplete due to various reasons, such as sensor failures or cloud cover. In this paper, we propose a set of time series data imputation methods for LAI values derived from satellite images by radiative transfer model (RTM) inversion. The methods perform temporal interpolation either at the level of individual pixels or on spatial aggregates. Our experimental evaluation demonstrates that our approach can be applied to various crop types and has the potential to improve the accuracy and timeliness of yield prediction.",

author = "Christoph J{\"o}rges and d'Hondt, {Jens E.} and Georgios Chatzigeorgakidis and Silke Migdall and Christian Miesgang and Susanne Karg and Heike Bach and Panagiotis Betchavas and Dimitrios Skoutas",

year = "2023",

month = nov,

day = "2",

doi = "10.2760/46796",

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pages = "373--376",

editor = "P. Soille and S. Lumnitz and S. Albani",

booktitle = "Proceedings of the 2023 conference on Big Data from Space (BIDS23)",

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Jörges, C, d'Hondt, JE, Chatzigeorgakidis, G, Migdall, S, Miesgang, C, Karg, S, Bach, H, Betchavas, P & Skoutas, D 2023, Leaf area index time series imputation for early yield prediction. in P Soille, S Lumnitz & S Albani (uitgave), Proceedings of the 2023 conference on Big Data from Space (BIDS23): From foresight to impact : 6-9 November 2023, Austrian Center, Vienna. Office for Official Publications of the EC, Luxembourg, blz. 373-376, 2023 Conference on Big Data from Space, BiDS’23, Vienna, Oostenrijk, 6/11/23. https://doi.org/10.2760/46796

Leaf area index time series imputation for early yield prediction. / Jörges, Christoph; d'Hondt, Jens E.; Chatzigeorgakidis, Georgios et al.
Proceedings of the 2023 conference on Big Data from Space (BIDS23): From foresight to impact : 6-9 November 2023, Austrian Center, Vienna. uitgave / P. Soille; S. Lumnitz; S. Albani. Luxembourg: Office for Official Publications of the EC, 2023. blz. 373-376.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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T1 - Leaf area index time series imputation for early yield prediction

AU - Jörges, Christoph

AU - d'Hondt, Jens E.

AU - Chatzigeorgakidis, Georgios

AU - Migdall, Silke

AU - Miesgang, Christian

AU - Karg, Susanne

AU - Bach, Heike

AU - Betchavas, Panagiotis

AU - Skoutas, Dimitrios

PY - 2023/11/2

Y1 - 2023/11/2

N2 - Leaf Area Index (LAI) is a key parameter in crop growth models, and its accurate estimation is crucial for yield prediction. However, LAI data values are often missing or incomplete due to various reasons, such as sensor failures or cloud cover. In this paper, we propose a set of time series data imputation methods for LAI values derived from satellite images by radiative transfer model (RTM) inversion. The methods perform temporal interpolation either at the level of individual pixels or on spatial aggregates. Our experimental evaluation demonstrates that our approach can be applied to various crop types and has the potential to improve the accuracy and timeliness of yield prediction.

AB - Leaf Area Index (LAI) is a key parameter in crop growth models, and its accurate estimation is crucial for yield prediction. However, LAI data values are often missing or incomplete due to various reasons, such as sensor failures or cloud cover. In this paper, we propose a set of time series data imputation methods for LAI values derived from satellite images by radiative transfer model (RTM) inversion. The methods perform temporal interpolation either at the level of individual pixels or on spatial aggregates. Our experimental evaluation demonstrates that our approach can be applied to various crop types and has the potential to improve the accuracy and timeliness of yield prediction.

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M3 - Conference contribution

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BT - Proceedings of the 2023 conference on Big Data from Space (BIDS23)

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A2 - Lumnitz, S.

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CY - Luxembourg

T2 - 2023 Conference on Big Data from Space, BiDS’23

Y2 - 6 November 2023 through 9 November 2023

ER -

Jörges C, d'Hondt JE, Chatzigeorgakidis G, Migdall S, Miesgang C, Karg S et al. Leaf area index time series imputation for early yield prediction. In Soille P, Lumnitz S, Albani S, redacteurs, Proceedings of the 2023 conference on Big Data from Space (BIDS23): From foresight to impact : 6-9 November 2023, Austrian Center, Vienna. Luxembourg: Office for Official Publications of the EC. 2023. blz. 373-376 doi: 10.2760/46796

Leaf area index time series imputation for early yield prediction

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