Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring

Yuval Sadeh; Xuan Zhu; David Dunkerley; Jeffrey P. Walker; Yuxi Zhang; Offer Rozenstein; V. S. Manivasagam; Karine Chenu

doi:10.1109/IGARSS39084.2020.9324336

Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring

Yuval Sadeh
, Xuan Zhu
, David Dunkerley
, Jeffrey P. Walker
, Yuxi Zhang
, Offer Rozenstein
, V. S. Manivasagam
, Karine Chenu

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

2 Scopus citations

Abstract

Remote-sensing applications are limited by the tradeoff between spatial and temporal resolutions. Monitoring the dynamics of Leaf Area Index (LAI) from space is a key attribute to estimate crop types and their phenology over large areas, and in characterising spatial variations within growers' fields. This paper proposes a new method to fuse a time-series of Sentinel-2 and CubeSat imagery into daily RGB-NIR surface reflectance and subsequently LAI datasets at 3 m resolution. The results of this study, which focused on monitoring wheat LAI, show that this method is effective (RMSE of 0.58-0.76) for high spatio-temporal monitoring of field-crops.

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
Pages	5274-5277
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9324336
State	Published - 26 Sep 2020
Externally published	Yes
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: 26 Sep 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

CubeSat
Data fusion
LAI
Monitoring
Phenotyping
Remote sensing
Yield prediction

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

Access to Document

10.1109/IGARSS39084.2020.9324336

Cite this

Sadeh, Y., Zhu, X., Dunkerley, D., Walker, J. P., Zhang, Y., Rozenstein, O., Manivasagam, V. S., & Chenu, K. (2020). Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 5274-5277). Article 9324336 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IGARSS39084.2020.9324336

Sadeh, Yuval ; Zhu, Xuan ; Dunkerley, David et al. / Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers, 2020. pp. 5274-5277 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{c5fb9b3ac9b34442a2263b74d6642b6c,

title = "Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring",

abstract = "Remote-sensing applications are limited by the tradeoff between spatial and temporal resolutions. Monitoring the dynamics of Leaf Area Index (LAI) from space is a key attribute to estimate crop types and their phenology over large areas, and in characterising spatial variations within growers' fields. This paper proposes a new method to fuse a time-series of Sentinel-2 and CubeSat imagery into daily RGB-NIR surface reflectance and subsequently LAI datasets at 3 m resolution. The results of this study, which focused on monitoring wheat LAI, show that this method is effective (RMSE of 0.58-0.76) for high spatio-temporal monitoring of field-crops.",

keywords = "CubeSat, Data fusion, LAI, Monitoring, Phenotyping, Remote sensing, Yield prediction",

author = "Yuval Sadeh and Xuan Zhu and David Dunkerley and Walker, \{Jeffrey P.\} and Yuxi Zhang and Offer Rozenstein and Manivasagam, \{V. S.\} and Karine Chenu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",

year = "2020",

month = sep,

day = "26",

doi = "10.1109/IGARSS39084.2020.9324336",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers",

pages = "5274--5277",

booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",

address = "United States",

}

Sadeh, Y, Zhu, X, Dunkerley, D, Walker, JP, Zhang, Y, Rozenstein, O, Manivasagam, VS & Chenu, K 2020, Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9324336, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers, pp. 5274-5277, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 26/09/20. https://doi.org/10.1109/IGARSS39084.2020.9324336

Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring. / Sadeh, Yuval; Zhu, Xuan; Dunkerley, David et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers, 2020. p. 5274-5277 9324336 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

TY - GEN

T1 - Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring

AU - Sadeh, Yuval

AU - Zhu, Xuan

AU - Dunkerley, David

AU - Walker, Jeffrey P.

AU - Zhang, Yuxi

AU - Rozenstein, Offer

AU - Manivasagam, V. S.

AU - Chenu, Karine

PY - 2020/9/26

Y1 - 2020/9/26

N2 - Remote-sensing applications are limited by the tradeoff between spatial and temporal resolutions. Monitoring the dynamics of Leaf Area Index (LAI) from space is a key attribute to estimate crop types and their phenology over large areas, and in characterising spatial variations within growers' fields. This paper proposes a new method to fuse a time-series of Sentinel-2 and CubeSat imagery into daily RGB-NIR surface reflectance and subsequently LAI datasets at 3 m resolution. The results of this study, which focused on monitoring wheat LAI, show that this method is effective (RMSE of 0.58-0.76) for high spatio-temporal monitoring of field-crops.

AB - Remote-sensing applications are limited by the tradeoff between spatial and temporal resolutions. Monitoring the dynamics of Leaf Area Index (LAI) from space is a key attribute to estimate crop types and their phenology over large areas, and in characterising spatial variations within growers' fields. This paper proposes a new method to fuse a time-series of Sentinel-2 and CubeSat imagery into daily RGB-NIR surface reflectance and subsequently LAI datasets at 3 m resolution. The results of this study, which focused on monitoring wheat LAI, show that this method is effective (RMSE of 0.58-0.76) for high spatio-temporal monitoring of field-crops.

KW - CubeSat

KW - Data fusion

KW - LAI

KW - Monitoring

KW - Phenotyping

KW - Remote sensing

KW - Yield prediction

UR - https://www.scopus.com/pages/publications/85101981985

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DO - 10.1109/IGARSS39084.2020.9324336

M3 - Conference contribution

AN - SCOPUS:85101981985

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 5274

EP - 5277

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

PB - Institute of Electrical and Electronics Engineers

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

Y2 - 26 September 2020 through 2 October 2020

ER -

Sadeh Y, Zhu X, Dunkerley D, Walker JP, Zhang Y, Rozenstein O et al. Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers. 2020. p. 5274-5277. 9324336. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9324336

Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this