Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield

Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In th...

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Autores: Segarra Torruella, Joel, Araus Ortega, José Luis, Kefauver, Shawn Carlisle
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/196721
Acesso em linha:https://hdl.handle.net/2445/196721
Access Level:acceso abierto
Palavra-chave:Blat
Agricultura de precisió
Wheat
Precision agriculture
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spelling Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yieldSegarra Torruella, JoelAraus Ortega, José LuisKefauver, Shawn CarlisleBlatAgricultura de precisióWheatPrecision agricultureWheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R2 and low RMSE, with potential for precision farming management before harvest.Elsevier2023202320222023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion12 p.application/pdfhttps://hdl.handle.net/2445/196721Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.jag.2022.102697International Journal of Applied Earth Observation and Geoinformation, 2022, vol. 107, p. 102697https://doi.org/10.1016/j.jag.2022.102697cc-by (c) Segarra, Joel et al., 2022https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1967212026-05-29T05:05:01Z
dc.title.none.fl_str_mv Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
title Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
spellingShingle Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
Segarra Torruella, Joel
Blat
Agricultura de precisió
Wheat
Precision agriculture
title_short Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
title_full Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
title_fullStr Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
title_full_unstemmed Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
title_sort Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
dc.creator.none.fl_str_mv Segarra Torruella, Joel
Araus Ortega, José Luis
Kefauver, Shawn Carlisle
author Segarra Torruella, Joel
author_facet Segarra Torruella, Joel
Araus Ortega, José Luis
Kefauver, Shawn Carlisle
author_role author
author2 Araus Ortega, José Luis
Kefauver, Shawn Carlisle
author2_role author
author
dc.subject.none.fl_str_mv Blat
Agricultura de precisió
Wheat
Precision agriculture
topic Blat
Agricultura de precisió
Wheat
Precision agriculture
description Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R2 and low RMSE, with potential for precision farming management before harvest.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/196721
url https://hdl.handle.net/2445/196721
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.jag.2022.102697
International Journal of Applied Earth Observation and Geoinformation, 2022, vol. 107, p. 102697
https://doi.org/10.1016/j.jag.2022.102697
dc.rights.none.fl_str_mv cc-by (c) Segarra, Joel et al., 2022
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Segarra, Joel et al., 2022
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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