High resolution wheat yield mapping using Sentinel-2

Hunt, Merryn and Blackburn, Alan and Carrasco, Luis and Redhead, John W. and Rowland, Clare S. (2019) High resolution wheat yield mapping using Sentinel-2. Remote Sensing of Environment, 233: 111410. ISSN 0034-4257

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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.

Item Type:
Journal Article
Journal or Publication Title:
Remote Sensing of Environment
Additional Information:
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
Uncontrolled Keywords:
?? yield estimationsentinel-2yield mappingrandom forest regressioncombine harvestersoil sciencecomputers in earth sciencesgeology ??
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Deposited On:
09 Sep 2019 10:05
Last Modified:
31 Dec 2023 01:07