Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery

M. F. Celik; O. Yuzugullu; E. Erten

doi:10.1109/IGARSS.2018.8651417

Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery

M. F. Celik, O. Yuzugullu, E. Erten

Department of Geomatics Engineering

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

1 Citation (Scopus)

Abstract

Crop phenology monitoring using Synthetic Aperture Radar (SAR) data is gaining popularity within the remote sensing community due to SAR's all weather and large coverage imaging capability. This paper introduces a polynomial chaos expansion (PCE) based regression algorithm to retrieve BBCH scale of crops, which identifies the phenology of crops in a standardized system. The impact and applicability of the proposed methodology is successfully illustrated using the TerraSAR-X dual-pol imagery that was acquired over the cultivation period of paddy-rice fields located in Turkey. To assess the applicability of the methodology, root mean square and correlation analysis were performed under different amount of training data and number of inputs.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9363-9366
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8651417
Publication status	Published - 31 Oct 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Crop phenology
Metamodels
Monitoring
Optimization
Polarimetry
Precision agriculture
SAR

UN SDGs

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

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10.1109/IGARSS.2018.8651417

Cite this

Celik, M. F., Yuzugullu, O., & Erten, E. (2018). Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 9363-9366). Article 8651417 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8651417

Celik, M. F. ; Yuzugullu, O. ; Erten, E. / Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 9363-9366 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery",

abstract = "Crop phenology monitoring using Synthetic Aperture Radar (SAR) data is gaining popularity within the remote sensing community due to SAR's all weather and large coverage imaging capability. This paper introduces a polynomial chaos expansion (PCE) based regression algorithm to retrieve BBCH scale of crops, which identifies the phenology of crops in a standardized system. The impact and applicability of the proposed methodology is successfully illustrated using the TerraSAR-X dual-pol imagery that was acquired over the cultivation period of paddy-rice fields located in Turkey. To assess the applicability of the methodology, root mean square and correlation analysis were performed under different amount of training data and number of inputs.",

keywords = "Crop phenology, Metamodels, Monitoring, Optimization, Polarimetry, Precision agriculture, SAR",

author = "Celik, {M. F.} and O. Yuzugullu and E. Erten",

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Celik, MF, Yuzugullu, O & Erten, E 2018, Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8651417, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 9363-9366, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8651417

Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery. / Celik, M. F.; Yuzugullu, O.; Erten, E.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 9363-9366 8651417 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

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

TY - GEN

T1 - Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery

AU - Celik, M. F.

AU - Yuzugullu, O.

AU - Erten, E.

PY - 2018/10/31

Y1 - 2018/10/31

N2 - Crop phenology monitoring using Synthetic Aperture Radar (SAR) data is gaining popularity within the remote sensing community due to SAR's all weather and large coverage imaging capability. This paper introduces a polynomial chaos expansion (PCE) based regression algorithm to retrieve BBCH scale of crops, which identifies the phenology of crops in a standardized system. The impact and applicability of the proposed methodology is successfully illustrated using the TerraSAR-X dual-pol imagery that was acquired over the cultivation period of paddy-rice fields located in Turkey. To assess the applicability of the methodology, root mean square and correlation analysis were performed under different amount of training data and number of inputs.

AB - Crop phenology monitoring using Synthetic Aperture Radar (SAR) data is gaining popularity within the remote sensing community due to SAR's all weather and large coverage imaging capability. This paper introduces a polynomial chaos expansion (PCE) based regression algorithm to retrieve BBCH scale of crops, which identifies the phenology of crops in a standardized system. The impact and applicability of the proposed methodology is successfully illustrated using the TerraSAR-X dual-pol imagery that was acquired over the cultivation period of paddy-rice fields located in Turkey. To assess the applicability of the methodology, root mean square and correlation analysis were performed under different amount of training data and number of inputs.

KW - Crop phenology

KW - Metamodels

KW - Monitoring

KW - Optimization

KW - Polarimetry

KW - Precision agriculture

KW - SAR

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M3 - Conference contribution

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T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Celik MF, Yuzugullu O, Erten E. Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 9363-9366. 8651417. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8651417

Regression based polynomial chaos expansion for crop phenology estimation coupled with polsar imagery

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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