Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Detalhes bibliográficos
Autores: Chiozza, M. V., Sánchez-Fernández, Luis, Pérez Ruiz, Manuel, Egea Cegarra, Gregorio
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:dnet:idus________::d084a12f9086a425e4d976a15efc1753
Acesso em linha:https://hdl.handle.net/11441/186453
https://doi.org/10.1016/j.atech.2026.101794
https://doi.org/10.1016/j.atech.2026.101840
Access Level:acceso abierto
Palavra-chave:Hyperspectral reflectance
High-throughput phenotyping
Precision agriculture
Photosynthesis
Stomatal conductance
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spelling Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor dataChiozza, M. V.Sánchez-Fernández, LuisPérez Ruiz, ManuelEgea Cegarra, GregorioHyperspectral reflectanceHigh-throughput phenotypingPrecision agriculturePhotosynthesisStomatal conductanceThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Understanding the soil-plant-atmosphere continuum (SPAC) is essential for breeding and advancing precision agriculture. Despite advances in hyperspectral monitoring, few studies have captured dynamic photosynthetic traits, such as net photosynthetic rate (A) and stomatal conductance (Gs), limiting insight into their temporal fluctuations and utility in breeding for stress resilience. This study integrates plant, soil and atmosphere sensor data, with statistical modelling to monitor season-long, fine-scale physiological and environmental variables, including A, Gs, vapor pressure deficit, soil moisture and crop water stress. A multi-sensor high-throughput phenotyping platform (HTPP) with a novel soil moisture system enabled high-resolution monitoring. Partial least squares regression (PLSR) models were used to predict photosynthetic traits from hyperspectral bands (∼400–1000 nm) and selected 20 vegetation indices (VIs). Temporal dynamics of both observed and predicted values were fitted using generalized additive models (GAMs) to describe the seasonal trajectories of photosynthetic traits, crop stress status and soil moisture across genotypes and water regimes. In wheat field trials, hyperspectral data predicted A and Gs with high accuracy (Root mean square error of prediction 3.71 and 58.93, respectively; R-squared 0.72 and 0.70, respectively) and the predicted temporal dynamics closely matched ground-truth measurements. Additionally, soil moisture and crop water status were monitored throughout the season, along with physiological traits. This approach provides scalable, data-driven solutions to support breeding for resilient cultivars and improvements in crop management, as the predicted data can be integrated into mechanistic crop models to establish empirical relationships with parameters that vary throughout the growing season.ElsevierIngeniería Aeroespacial y Mecánica de FluidosMinisterio de Ciencia, Innovación y Universidades (MICIU). España2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/186453https://doi.org/10.1016/j.atech.2026.101794https://doi.org/10.1016/j.atech.2026.101840reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSmart Agricultural Technology, 13, 101794. Chiozza, M. V., Sánchez-Fernández, L., Pérez-Ruiz, M., & Egea, G. (2026). Corrigendum to “Tracking temporal variations in the soil–plant–atmosphere continuum in wheat using multisensor” [Smart Agricultural Technology, Volume 13 (2026), Pages 1–12/101794]. Smart Agricultural Technology, 13, 101840. https://doi.org/10.1016/j.atech.2026.101840PID2021-125080OB-I00https://www.sciencedirect.com/science/article/pii/S2772375526000183info:eu-repo/semantics/openAccessoai:dnet:idus________::d084a12f9086a425e4d976a15efc17532026-06-17T12:51:07Z
dc.title.none.fl_str_mv Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
title Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
spellingShingle Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
Chiozza, M. V.
Hyperspectral reflectance
High-throughput phenotyping
Precision agriculture
Photosynthesis
Stomatal conductance
title_short Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
title_full Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
title_fullStr Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
title_full_unstemmed Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
title_sort Tracking temporal variations in the soil-plant-atmosphere continuum in wheat using multisensor data
dc.creator.none.fl_str_mv Chiozza, M. V.
Sánchez-Fernández, Luis
Pérez Ruiz, Manuel
Egea Cegarra, Gregorio
author Chiozza, M. V.
author_facet Chiozza, M. V.
Sánchez-Fernández, Luis
Pérez Ruiz, Manuel
Egea Cegarra, Gregorio
author_role author
author2 Sánchez-Fernández, Luis
Pérez Ruiz, Manuel
Egea Cegarra, Gregorio
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Aeroespacial y Mecánica de Fluidos
Ministerio de Ciencia, Innovación y Universidades (MICIU). España
dc.subject.none.fl_str_mv Hyperspectral reflectance
High-throughput phenotyping
Precision agriculture
Photosynthesis
Stomatal conductance
topic Hyperspectral reflectance
High-throughput phenotyping
Precision agriculture
Photosynthesis
Stomatal conductance
description This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
publishDate 2026
dc.date.none.fl_str_mv 2026
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/11441/186453
https://doi.org/10.1016/j.atech.2026.101794
https://doi.org/10.1016/j.atech.2026.101840
url https://hdl.handle.net/11441/186453
https://doi.org/10.1016/j.atech.2026.101794
https://doi.org/10.1016/j.atech.2026.101840
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Smart Agricultural Technology, 13, 101794.
Chiozza, M. V., Sánchez-Fernández, L., Pérez-Ruiz, M., & Egea, G. (2026). Corrigendum to “Tracking temporal variations in the soil–plant–atmosphere continuum in wheat using multisensor” [Smart Agricultural Technology, Volume 13 (2026), Pages 1–12/101794]. Smart Agricultural Technology, 13, 101840. https://doi.org/10.1016/j.atech.2026.101840
PID2021-125080OB-I00
https://www.sciencedirect.com/science/article/pii/S2772375526000183
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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