Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection

Mattia Stasolla; Xavier Neyt

doi:10.1109/IGARSS.2019.8898706

Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection

Mattia Stasolla, Xavier Neyt

Remote Sensing

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

Abstract

Sugarcane is the world's largest crop by production quantity, as reported by the Food and Agriculture Organization of the United Nations. Its growth cycle has a duration of about 12-14 months, and the same plantation can be generally harvested up to 7 times before replanting is needed. In order to both predict the yield and optimize the production processes, sugarcane industries need to be regularly updated on the harvest progress; however, they mainly rely on direct communications from farmers, and this has evident limitations.In this paper we present a method that exploits stacks of Sentinel-1 images for the automatic detection of sugarcane harvest dates. The method has been used to monitor, over a period of 21 months, a large cultivated area in Northern Senegal that comprises 719 sugarcane parcels.The results have shown that the method performs well in terms of both detection and estimation accuracy, and has the potential to be operationally used in the sugarcane production processes.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1594-1597
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898706
Publication status	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Keywords

GRD
Sentinel-1
harvest detection.
sugarcane
time series analysis

Access to Document

10.1109/IGARSS.2019.8898706

Cite this

Stasolla, M., & Neyt, X. (2019). Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 1594-1597). Article 8898706 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8898706

@inproceedings{ac5873d7281d4cd58a84e4c08a36532e,

title = "Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection",

abstract = "Sugarcane is the world's largest crop by production quantity, as reported by the Food and Agriculture Organization of the United Nations. Its growth cycle has a duration of about 12-14 months, and the same plantation can be generally harvested up to 7 times before replanting is needed. In order to both predict the yield and optimize the production processes, sugarcane industries need to be regularly updated on the harvest progress; however, they mainly rely on direct communications from farmers, and this has evident limitations.In this paper we present a method that exploits stacks of Sentinel-1 images for the automatic detection of sugarcane harvest dates. The method has been used to monitor, over a period of 21 months, a large cultivated area in Northern Senegal that comprises 719 sugarcane parcels.The results have shown that the method performs well in terms of both detection and estimation accuracy, and has the potential to be operationally used in the sugarcane production processes.",

keywords = "GRD, Sentinel-1, harvest detection., sugarcane, time series analysis",

author = "Mattia Stasolla and Xavier Neyt",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

month = jul,

doi = "10.1109/IGARSS.2019.8898706",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1594--1597",

booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",

}

Stasolla, M & Neyt, X 2019, Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8898706, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 1594-1597, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8898706

Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection. / Stasolla, Mattia ; Neyt, Xavier.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1594-1597 8898706 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

TY - GEN

T1 - Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection

AU - Stasolla, Mattia

AU - Neyt, Xavier

PY - 2019/7

Y1 - 2019/7

N2 - Sugarcane is the world's largest crop by production quantity, as reported by the Food and Agriculture Organization of the United Nations. Its growth cycle has a duration of about 12-14 months, and the same plantation can be generally harvested up to 7 times before replanting is needed. In order to both predict the yield and optimize the production processes, sugarcane industries need to be regularly updated on the harvest progress; however, they mainly rely on direct communications from farmers, and this has evident limitations.In this paper we present a method that exploits stacks of Sentinel-1 images for the automatic detection of sugarcane harvest dates. The method has been used to monitor, over a period of 21 months, a large cultivated area in Northern Senegal that comprises 719 sugarcane parcels.The results have shown that the method performs well in terms of both detection and estimation accuracy, and has the potential to be operationally used in the sugarcane production processes.

AB - Sugarcane is the world's largest crop by production quantity, as reported by the Food and Agriculture Organization of the United Nations. Its growth cycle has a duration of about 12-14 months, and the same plantation can be generally harvested up to 7 times before replanting is needed. In order to both predict the yield and optimize the production processes, sugarcane industries need to be regularly updated on the harvest progress; however, they mainly rely on direct communications from farmers, and this has evident limitations.In this paper we present a method that exploits stacks of Sentinel-1 images for the automatic detection of sugarcane harvest dates. The method has been used to monitor, over a period of 21 months, a large cultivated area in Northern Senegal that comprises 719 sugarcane parcels.The results have shown that the method performs well in terms of both detection and estimation accuracy, and has the potential to be operationally used in the sugarcane production processes.

KW - GRD

KW - Sentinel-1

KW - harvest detection.

KW - sugarcane

KW - time series analysis

UR - http://www.scopus.com/inward/record.url?scp=85077723915&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2019.8898706

DO - 10.1109/IGARSS.2019.8898706

M3 - Conference contribution

AN - SCOPUS:85077723915

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 1594

EP - 1597

BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

Stasolla M , Neyt X. Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1594-1597. 8898706. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8898706

Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

EORegions-Science: EORegions-Science

Cite this

Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

EORegions-Science: EORegions-Science

Cite this