A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

Yuri Shendryk; Lecheng Pan; Matthew Craigie; Mattia Stasolla; Catherine Ticehurst; Peter Thorburn

doi:10.1109/IGARSS39084.2020.9323418

A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

Yuri Shendryk, Lecheng Pan, Matthew Craigie, Mattia Stasolla, Catherine Ticehurst, Peter Thorburn

Remote Sensing

Agriculture and Food

Résultats de recherche: Chapitre dans un livre, un rapport, des actes de conférences › Contribution à une conférence › Revue par des pairs

Résumé

An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

langue originale	Anglais
titre	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	5167-5170
Nombre de pages	4
ISBN (Electronique)	9781728163741
Les DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323418
état	Publié - 26 sept. 2020
Evénement	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, États-Unis Durée: 26 sept. 2020 → 2 oct. 2020

Série de publications

Nom	International Geoscience and Remote Sensing Symposium (IGARSS)

Une conférence

Une conférence	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Pays/Territoire	États-Unis
La ville	Virtual, Waikoloa
période	26/09/20 → 2/10/20

Accès au document

10.1109/IGARSS39084.2020.9323418

Autres fichiers et liens

Lien vers la publication dans Scopus

EORegions-Science: EORegions-Science
Neyt, X. (Promoteur) & Stasolla, M. (Chercheur)
1/11/16 → 31/08/18
Projet: Recherche

Contient cette citation

Shendryk, Y., Pan, L., Craigie, M., Stasolla, M., Ticehurst, C., & Thorburn, P. (2020). A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. Dans 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (p. 5167-5170). Article 9323418 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9323418

Shendryk, Yuri ; Pan, Lecheng ; Craigie, Matthew et al. / A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 5167-5170 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane",

abstract = "An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.",

keywords = "SAR, Sentinel-1, Sentinel-2, fusion, harvest date, multispectral, satellite, sugarcane, time-series, yield",

author = "Yuri Shendryk and Lecheng Pan and Matthew Craigie and Mattia Stasolla and Catherine Ticehurst and Peter Thorburn",

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doi = "10.1109/IGARSS39084.2020.9323418",

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Shendryk, Y, Pan, L, Craigie, M, Stasolla, M, Ticehurst, C & Thorburn, P 2020, A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. Dans 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9323418, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., p. 5167-5170, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, États-Unis, 26/09/20. https://doi.org/10.1109/IGARSS39084.2020.9323418

A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. / Shendryk, Yuri; Pan, Lecheng; Craigie, Matthew et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 5167-5170 9323418 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Résultats de recherche: Chapitre dans un livre, un rapport, des actes de conférences › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

AU - Shendryk, Yuri

AU - Pan, Lecheng

AU - Craigie, Matthew

AU - Stasolla, Mattia

AU - Ticehurst, Catherine

AU - Thorburn, Peter

PY - 2020/9/26

Y1 - 2020/9/26

N2 - An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

AB - An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

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KW - harvest date

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KW - satellite

KW - sugarcane

KW - time-series

KW - yield

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BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020

Y2 - 26 September 2020 through 2 October 2020

ER -

Shendryk Y, Pan L, Craigie M, Stasolla M, Ticehurst C, Thorburn P. A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane. Dans 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 5167-5170. 9323418. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9323418

A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Projets

EORegions-Science: EORegions-Science

Contient cette citation