High resolution wheat yield mapping using Sentinel-2

Hunt, Merryn L. ORCID: https://orcid.org/0000-0003-4435-3644; Blackburn, George Alan; Carrasco, Luis; Redhead, John W. ORCID: https://orcid.org/0000-0002-2233-3848; Rowland, Clare S. ORCID: https://orcid.org/0000-0002-0459-506X. 2019 High resolution wheat yield mapping using Sentinel-2. Remote Sensing of Environment, 233, 111410. 15, pp. https://doi.org/10.1016/j.rse.2019.111410

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Official URL: http://dx.doi.org/10.1016/j.rse.2019.111410

Abstract/Summary

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:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.rse.2019.111410
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-) Soils and Land Use (Science Area 2017-)
ISSN:	0034-4257
Additional Keywords:	yield estimation, Sentinel-2, yield mapping, random forest regression, combine harvester
NORA Subject Terms:	Agriculture and Soil Science
Date made live:	26 Sep 2019 12:42 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/525234

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.rse.2019.111410

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)
Soils and Land Use (Science Area 2017-)

ISSN:

0034-4257

Additional Keywords:

yield estimation, Sentinel-2, yield mapping, random forest regression, combine harvester

NORA Subject Terms:

Agriculture and Soil Science