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Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

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dc.abstract.enRemote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorSchlerf, Martin
dc.contributor.authorStancile, Gheorghe
dc.contributor.authorFilchev, Lachezar
dc.contributor.authorRozenstein, Offer
dc.contributor.authorTomelleri, Enrico
dc.contributor.authorSulis, Mauro
dc.contributor.authorBandopadhyay, Subhajit
dc.contributor.authorPôças, Isabel
dc.contributor.authorGarcia, Monica
dc.contributor.authorAasen, Helge
dc.contributor.authorDarvishzadeh, Roshanak
dc.contributor.authorHank, Tobias
dc.contributor.authorTagliabue, Giulia
dc.contributor.authorAbdelbaki, Asmaa
dc.contributor.authorSiegmann, Bastian
dc.contributor.authorFoerster, Michael
dc.contributor.authorRossini, Micol
dc.contributor.authorGormus, Esra Tunc
dc.contributor.authorKoren, Gerbrand
dc.contributor.authorCelesti, Marco
dc.contributor.authorHalabuk, Andrej
dc.contributor.authorBuchaillot, Ma. Luisa
dc.contributor.authorPrikaziuk, Egor
dc.contributor.authorPieruschka, Roland
dc.contributor.authorFahrner, Sven
dc.contributor.authorPaz, Veronica Sobejano
dc.contributor.authorHerrmann, Ittai
dc.contributor.authorRascher, Uwe
dc.contributor.authorDamm, Alexander
dc.contributor.authorTol, Christiaan van der
dc.contributor.authorAtzberger, Clement
dc.contributor.authorVerrelst, Jochem
dc.contributor.authorGerhards, Max
dc.contributor.authorWittenberghe, Shari Van
dc.contributor.authorKefauver, Shawn C.
dc.contributor.authorKycko, Marlena
dc.contributor.authorMachwitz, Miriam
dc.contributor.authorBerger, Katja
dc.date.accessioned2024-01-25T13:17:32Z
dc.date.available2024-01-25T13:17:32Z
dc.date.copyright2022-08-04
dc.date.issued2022
dc.description.accesstimeAT_PUBLICATION
dc.description.financePublikacja bezkosztowa
dc.description.versionFINAL_PUBLISHED
dc.description.volume280
dc.identifier.doi10.1016/J.RSE.2022.113198
dc.identifier.issn0034-4257
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/113135
dc.identifier.weblinkhttps://api.elsevier.com/content/article/PII:S003442572200308X?httpAccept=text/xml
dc.languageeng
dc.pbn.affiliationearth and related environmental sciences
dc.relation.ispartofRemote Sensing of Environment
dc.relation.pages113198
dc.rightsCC-BY-NC
dc.sciencecloudnosend
dc.subject.enPrecision agriculture multi-modal solar-induced fluorescence satellite hyperspectral multispectral biotic and abiotic stress
dc.titleMulti-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review
dc.typeJournalArticle
dspace.entity.typePublication