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    Rights statement: This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 233, 2019 DOI: 10.1016/j.rse.2019.111410

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High resolution wheat yield mapping using Sentinel-2

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High resolution wheat yield mapping using Sentinel-2. / Hunt, Merryn; Blackburn, Alan; Carrasco, Luis et al.
In: Remote Sensing of Environment, Vol. 233, 111410, 01.11.2019.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hunt, M, Blackburn, A, Carrasco, L, Redhead, JW & Rowland, CS 2019, 'High resolution wheat yield mapping using Sentinel-2', Remote Sensing of Environment, vol. 233, 111410. https://doi.org/10.1016/j.rse.2019.111410

APA

Hunt, M., Blackburn, A., Carrasco, L., Redhead, J. W., & Rowland, C. S. (2019). High resolution wheat yield mapping using Sentinel-2. Remote Sensing of Environment, 233, Article 111410. https://doi.org/10.1016/j.rse.2019.111410

Vancouver

Hunt M, Blackburn A, Carrasco L, Redhead JW, Rowland CS. High resolution wheat yield mapping using Sentinel-2. Remote Sensing of Environment. 2019 Nov 1;233:111410. Epub 2019 Sept 9. doi: 10.1016/j.rse.2019.111410

Author

Hunt, Merryn ; Blackburn, Alan ; Carrasco, Luis et al. / High resolution wheat yield mapping using Sentinel-2. In: Remote Sensing of Environment. 2019 ; Vol. 233.

Bibtex

@article{c70a3b424c4b4002970199b9b9069bd9,
title = "High resolution wheat yield mapping using Sentinel-2",
abstract = "Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t.",
keywords = "Yield estimation, Sentinel-2, Yield mapping, Random forest regression, Combine harvester",
author = "Merryn Hunt and Alan Blackburn and Luis Carrasco and Redhead, {John W.} and Rowland, {Clare S.}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 233, 2019 DOI: 10.1016/j.rse.2019.111410",
year = "2019",
month = nov,
day = "1",
doi = "10.1016/j.rse.2019.111410",
language = "English",
volume = "233",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - High resolution wheat yield mapping using Sentinel-2

AU - Hunt, Merryn

AU - Blackburn, Alan

AU - Carrasco, Luis

AU - Redhead, John W.

AU - Rowland, Clare S.

N1 - This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 233, 2019 DOI: 10.1016/j.rse.2019.111410

PY - 2019/11/1

Y1 - 2019/11/1

N2 - Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t.

AB - Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t.

KW - Yield estimation

KW - Sentinel-2

KW - Yield mapping

KW - Random forest regression

KW - Combine harvester

U2 - 10.1016/j.rse.2019.111410

DO - 10.1016/j.rse.2019.111410

M3 - Journal article

VL - 233

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 111410

ER -