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

Autor: Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Global Ecohydrology and Sustainability, Environmental Sciences
Přispěvatelé: Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Global Ecohydrology and Sustainability, Environmental Sciences, Department of Water Resources, Digital Society Institute, Faculty of Geo-Information Science and Earth Observation, UT-I-ITC-WCC, Department of Natural Resources, UT-I-ITC-FORAGES
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Remote sensing of environment 280, 113198-(2022). doi:10.1016/j.rse.2022.113198
Berger, K., Machwitz, M., Kycko, M., Kefauver, S. C., Van Wittenberghe, S., Gerhards, M., ... & Schlerf, M. (2022). Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote sensing of environment, 280, 113198.
Berger, K, Machwitz, M, Kycko, M, Kefauver, S C, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V S, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M L, Halabuk, A, Celesti, M, Koren, G, Gormus, E T, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G & Schlerf, M 2022, ' Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain : A review ', Remote Sensing of Environment, vol. 280, 113198 . https://doi.org/10.1016/j.rse.2022.113198
Remote sensing of environment, 280:113198. Elsevier
Remote Sensing of Environment, 280, 1. Elsevier
Remote Sensing of Environment
ISSN: 0034-4257
Popis: 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.
Databáze: OpenAIRE
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