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Monitoring winter wheat growth performance at sub-field scale using multitemporal Sentinel-2 imagery

Monitoring winter wheat growth performance at sub-field scale using multitemporal Sentinel-2 imagery
Monitoring winter wheat growth performance at sub-field scale using multitemporal Sentinel-2 imagery
A crop growth monitoring system should objectively and reproducibly reflect changes in crop biophysical properties during the growing season. By monitoring crop growth and performance at specific crop development stages, the farmer can obtain reliable information for timely crop management to achieve optimum crop production. This work aimed to evaluate crop development using five winter wheat (Triticum aestivum L.) biophysical properties (shoots number, green area index, plant height, leaf N content, and aboveground dry biomass) predicted from Sentinel-2 data compared with benchmarks representing target growth from emergence to harvest. Data were collected for four principal phenology stages (tillering, stem elongation, heading, and fruit development) in 35 winter wheat fields in the Republic of Ireland and 40 in the United Kingdom in 2020 and 2021. A total of 1500 plots were selected for crop sampling over two growing seasons. The models were generally good, but phenology-specific models performed better (R2 between 0.72 and 0.87) than models for the entire season (R2 between 0.13 and 0.84). To assess the low-performance zones in fields, the predicted biophysical properties were compared to benchmarks taken from agronomic advice. Spatial analysis was then used to identify low-performance areas in fields, which were validated using farmers’ feedback. It was concluded that the approach taken could be reliably used to monitor winter wheat over a wide area and through time.
Precision agriculture, Crop monitoring, Remote sensing, Crop biophysical, Spectral bands, Spatial analysis
1569-8432
Goh, Bing-Bing
6c1b543b-bdc7-4d85-b7cc-d9ed2c9fc4d3
King, Peter
4d093807-f384-4129-97dc-a1bf1b520525
Whetton, Rebecca L.
34b41770-7c83-4dd8-93a1-520612ebe198
Sattari, Sheida Z.
c04f62fc-3b3c-4cf8-b09b-5e30eb5fab78
Holden, Nicholas M.
6c0e4712-7d44-498d-987d-4e43377d5837
Goh, Bing-Bing
6c1b543b-bdc7-4d85-b7cc-d9ed2c9fc4d3
King, Peter
4d093807-f384-4129-97dc-a1bf1b520525
Whetton, Rebecca L.
34b41770-7c83-4dd8-93a1-520612ebe198
Sattari, Sheida Z.
c04f62fc-3b3c-4cf8-b09b-5e30eb5fab78
Holden, Nicholas M.
6c0e4712-7d44-498d-987d-4e43377d5837

Goh, Bing-Bing, King, Peter, Whetton, Rebecca L., Sattari, Sheida Z. and Holden, Nicholas M. (2022) Monitoring winter wheat growth performance at sub-field scale using multitemporal Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation, 115, [103124]. (doi:10.1016/j.jag.2022.103124).

Record type: Article

Abstract

A crop growth monitoring system should objectively and reproducibly reflect changes in crop biophysical properties during the growing season. By monitoring crop growth and performance at specific crop development stages, the farmer can obtain reliable information for timely crop management to achieve optimum crop production. This work aimed to evaluate crop development using five winter wheat (Triticum aestivum L.) biophysical properties (shoots number, green area index, plant height, leaf N content, and aboveground dry biomass) predicted from Sentinel-2 data compared with benchmarks representing target growth from emergence to harvest. Data were collected for four principal phenology stages (tillering, stem elongation, heading, and fruit development) in 35 winter wheat fields in the Republic of Ireland and 40 in the United Kingdom in 2020 and 2021. A total of 1500 plots were selected for crop sampling over two growing seasons. The models were generally good, but phenology-specific models performed better (R2 between 0.72 and 0.87) than models for the entire season (R2 between 0.13 and 0.84). To assess the low-performance zones in fields, the predicted biophysical properties were compared to benchmarks taken from agronomic advice. Spatial analysis was then used to identify low-performance areas in fields, which were validated using farmers’ feedback. It was concluded that the approach taken could be reliably used to monitor winter wheat over a wide area and through time.

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More information

Accepted/In Press date: 20 November 2022
e-pub ahead of print date: 24 November 2022
Published date: December 2022
Keywords: Precision agriculture, Crop monitoring, Remote sensing, Crop biophysical, Spectral bands, Spatial analysis

Identifiers

Local EPrints ID: 484029
URI: http://eprints.soton.ac.uk/id/eprint/484029
ISSN: 1569-8432
PURE UUID: 9b4b8698-3713-474e-9773-2976761cd06e
ORCID for Bing-Bing Goh: ORCID iD orcid.org/0000-0001-7108-991X

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Date deposited: 09 Nov 2023 17:34
Last modified: 29 Aug 2024 02:06

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Contributors

Author: Bing-Bing Goh ORCID iD
Author: Peter King
Author: Rebecca L. Whetton
Author: Sheida Z. Sattari
Author: Nicholas M. Holden

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