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

Katja Berger*, Miriam Machwitz, Marlena Kycko, Shawn C. Kefauver, Shari Van Wittenberghe, Max Gerhards, Jochem Verrelst, Clement Atzberger, Christiaan van der Tol, Alexander Damm, Uwe Rascher, Ittai Herrmann, Veronica Sobejano Paz, Sven Fahrner, Roland Pieruschka, Egor Prikaziuk, Ma Luisa Buchaillot, Andrej Halabuk, Marco Celesti, Gerbrand KorenEsra Tunc Gormus, Micol Rossini, Michael Foerster, Bastian Siegmann, Asmaa Abdelbaki, Giulia Tagliabue, Tobias Hank, Roshanak Darvishzadeh, Helge Aasen, Monica Garcia, Isabel Pôças, Subhajit Bandopadhyay, Mauro Sulis, Enrico Tomelleri, Offer Rozenstein, Lachezar Filchev, Gheorghe Stancile, Martin Schlerf

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

28 Scopus citations


Remote 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.

Original languageAmerican English
Article number113198
JournalRemote Sensing of Environment
StatePublished - Oct 2022

Bibliographical note

Funding Information:
The research was mainly supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST ( European Cooperation in Science and Technology , (accessed on 16/03/2022)). The publication is also the result of the project implementation: “Scientific support of climate change adaptation in agriculture and mitigation of soil degradation” ( ITMS2014+313011W580 ) supported by the Integrated Infrastructure Operational Programme funded by the ERDF . We also thank the two reviewers for their fundamental suggestions.

Funding Information:
This research was funded by the EnMAP scientific preparation program under the DLR Space Administration with resources from the German Federal Ministry of Economic Affairs and Energy , grant number 50EE1923 (K. Berger). This research was also funded by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (grant agreement 755617 ) (K. Berger) and Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities) (J. Verrelst). S.C. Kefauver is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacià 3 n, Spain . S. Van Wittenberghe is supported by the European Research Council (ERC) under the ERC-2021-STG PHOTOFLUX project (grant agreement 101041768 ). M. Celesti was supported by a Living Planet Fellowship (ESA/Contract No. 4000125442/18/I-NS ) of the European Space Agency . V. Sobejano Paz PhD thesis was supported by the AgWIT JPI project ERA-NET Co-fund Water Works 2015 Call and a SDC grant ( Sino Danish Council ).

Publisher Copyright:
© 2022 The Authors


  • Precision agriculture multi-modal solar-induced fluorescence satellite hyperspectral multispectral biotic and abiotic stress


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