TY - JOUR
T1 - Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain
T2 - A review
AU - Berger, Katja
AU - Machwitz, Miriam
AU - Kycko, Marlena
AU - Kefauver, Shawn C.
AU - Van Wittenberghe, Shari
AU - Gerhards, Max
AU - Verrelst, Jochem
AU - Atzberger, Clement
AU - van der Tol, Christiaan
AU - Damm, Alexander
AU - Rascher, Uwe
AU - Herrmann, Ittai
AU - Paz, Veronica Sobejano
AU - Fahrner, Sven
AU - Pieruschka, Roland
AU - Prikaziuk, Egor
AU - Buchaillot, Ma. Luisa
AU - Halabuk, Andrej
AU - Celesti, Marco
AU - Koren, Gerbrand
AU - Gormus, Esra Tunc
AU - Rossini, Micol
AU - Foerster, Michael
AU - Siegmann, Bastian
AU - Abdelbaki, Asmaa
AU - Tagliabue, Giulia
AU - Hank, Tobias
AU - Darvishzadeh, Roshanak
AU - Aasen, Helge
AU - Garcia, Monica
AU - Pôças, Isabel
AU - Bandopadhyay, Subhajit
AU - Sulis, Mauro
AU - Tomelleri, Enrico
AU - Rozenstein, Offer
AU - Filchev, Lachezar
AU - Stancile, Gheorghe
AU - Schlerf, Martin
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Precision agriculture multi-modal solar-induced fluorescence satellite hyperspectral multispectral biotic and abiotic stress
U2 - 10.1016/j.rse.2022.113198
DO - 10.1016/j.rse.2022.113198
M3 - Review
C2 - 36090616
SN - 0034-4257
VL - 280
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 113198
ER -