Home > Research > Publications & Outputs > High resolution wheat yield mapping using Senti...

Electronic data

  • Author Accepted Manuscript

    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

    Accepted author manuscript, 2 MB, PDF document

    Embargo ends: 7/09/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

High resolution wheat yield mapping using Sentinel-2

Research output: Contribution to journalJournal article

Published
Close
Article number111410
<mark>Journal publication date</mark>1/11/2019
<mark>Journal</mark>Remote Sensing of Environment
Volume233
Number of pages15
Publication statusPublished
Early online date9/09/19
Original languageEnglish

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.

Bibliographic note

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