Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain

first_page settings Open AccessArticle Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain by Joel Segarra 1,2, Jon González-Torralba 3, Íker Aranjuelo 4 [OrcID] , Jose Luis Araus 1,2 [OrcID] and Shawn C...

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Autores: Segarra, Joel, González-Torralba, Jon, Aranjuelo Michelena, Iker, Araus Ortega, José Luis, Kefauver, Shawn C.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/69951
Acceso en línea:https://doi.org/10.3390/rs12142278
http://hdl.handle.net/10459.1/69951
Access Level:acceso abierto
Palabra clave:Remote sensing
Agriculture
Crop monitoring
Sentinel-2
Wheat
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
title Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
spellingShingle Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
Segarra, Joel
Remote sensing
Agriculture
Crop monitoring
Sentinel-2
Wheat
title_short Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
title_full Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
title_fullStr Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
title_full_unstemmed Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
title_sort Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
dc.creator.none.fl_str_mv Segarra, Joel
González-Torralba, Jon
Aranjuelo Michelena, Iker
Araus Ortega, José Luis
Kefauver, Shawn C.
author Segarra, Joel
author_facet Segarra, Joel
González-Torralba, Jon
Aranjuelo Michelena, Iker
Araus Ortega, José Luis
Kefauver, Shawn C.
author_role author
author2 González-Torralba, Jon
Aranjuelo Michelena, Iker
Araus Ortega, José Luis
Kefauver, Shawn C.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Remote sensing
Agriculture
Crop monitoring
Sentinel-2
Wheat
topic Remote sensing
Agriculture
Crop monitoring
Sentinel-2
Wheat
description first_page settings Open AccessArticle Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain by Joel Segarra 1,2, Jon González-Torralba 3, Íker Aranjuelo 4 [OrcID] , Jose Luis Araus 1,2 [OrcID] and Shawn C. Kefauver 1,2,* [OrcID] 1 Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain 2 AGROTECNIO (Center for Research in Agrotechnology), 25198 Lleida, Spain 3 Grupo AN, Campo Tajonar, 31192 Tajonar, Spain 4 Instituto de Agrobiotecnología (IdAB), CSIC-Gobierno de Navarra, 31192 Pamplona, Spain * Author to whom correspondence should be addressed. Remote Sens. 2020, 12(14), 2278; https://doi.org/10.3390/rs12142278 Received: 4 June 2020 / Revised: 10 July 2020 / Accepted: 13 July 2020 / Published: 15 July 2020 (This article belongs to the Special Issue Remote Sensing of Plant-Climate Interactions) Download PDF Browse Figures Review Reports Abstract Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.
publishDate 2020
dc.date.none.fl_str_mv 2020
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://doi.org/10.3390/rs12142278
http://hdl.handle.net/10459.1/69951
url https://doi.org/10.3390/rs12142278
http://hdl.handle.net/10459.1/69951
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106650RB-C21
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PCIN-2017-063
Reproducció del document publicat a: https://doi.org/10.3390/rs12142278
Remote Sensing, 2020, vol. 12, núm. 14, p. 2278
dc.rights.none.fl_str_mv cc-by (c) Segarra, Joel et al., 2020
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Segarra, Joel et al., 2020
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, SpainSegarra, JoelGonzález-Torralba, JonAranjuelo Michelena, IkerAraus Ortega, José LuisKefauver, Shawn C.Remote sensingAgricultureCrop monitoringSentinel-2Wheatfirst_page settings Open AccessArticle Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain by Joel Segarra 1,2, Jon González-Torralba 3, Íker Aranjuelo 4 [OrcID] , Jose Luis Araus 1,2 [OrcID] and Shawn C. Kefauver 1,2,* [OrcID] 1 Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain 2 AGROTECNIO (Center for Research in Agrotechnology), 25198 Lleida, Spain 3 Grupo AN, Campo Tajonar, 31192 Tajonar, Spain 4 Instituto de Agrobiotecnología (IdAB), CSIC-Gobierno de Navarra, 31192 Pamplona, Spain * Author to whom correspondence should be addressed. Remote Sens. 2020, 12(14), 2278; https://doi.org/10.3390/rs12142278 Received: 4 June 2020 / Revised: 10 July 2020 / Accepted: 13 July 2020 / Published: 15 July 2020 (This article belongs to the Special Issue Remote Sensing of Plant-Climate Interactions) Download PDF Browse Figures Review Reports Abstract Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.This study was supported by the Spanish projects PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovación and the IRUEC PCIN-2017-063 from the Ministerio de Economía y Competividad and by the project “Nuevas aplicaciones de agricultura de precisión para la monitorización del rendimiento del cultivo de trigo en Navarra” from the Government of Navarre, Spain (0011-1365-2018-000213/0011-1365-2018-000150).MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/rs12142278http://hdl.handle.net/10459.1/69951reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106650RB-C21info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PCIN-2017-063Reproducció del document publicat a: https://doi.org/10.3390/rs12142278Remote Sensing, 2020, vol. 12, núm. 14, p. 2278cc-by (c) Segarra, Joel et al., 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/699512026-06-24T12:42:17Z
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