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

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

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.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5167-5170
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323418
Publication status	Published - 26 Sept 2020
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: 26 Sept 2020 → 2 Oct 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	26/09/20 → 2/10/20

Keywords

SAR
Sentinel-1
Sentinel-2
fusion
harvest date
multispectral
satellite
sugarcane
time-series
yield

Access to Document

10.1109/IGARSS39084.2020.9323418

Cite this

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. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 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. pp. 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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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. in 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., pp. 5167-5170, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 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)).

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

TY - GEN

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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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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. In 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

Abstract

Publication series

Conference

Keywords

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Cite this

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

EORegions-Science: EORegions-Science

Cite this