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


Berger K., Machwitz M., Kycko M., Kefauver S. C., Van Wittenberghe S., Gerhards M., ...Daha Fazla

REMOTE SENSING OF ENVIRONMENT, cilt.280, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 280
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.rse.2022.113198
  • Dergi Adı: REMOTE SENSING OF ENVIRONMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Anahtar Kelimeler: Precision agriculture multi-modal solar-induced fluorescence satellite hyperspectral, multispectral biotic and abiotic stress, INDUCED CHLOROPHYLL FLUORESCENCE, INDUCED ABSORBENCY CHANGES, RADIATIVE-TRANSFER MODEL, REFLECTANCE INDEX PRI, WATER-STRESS, CANOPY TEMPERATURE, VEGETATION STRESS, AGRICULTURAL DROUGHT, XANTHOPHYLL CYCLE, NITROGEN-CONTENT
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agri-culture. Ongoing developments in optical remote sensing technologies have provided tools to increase our un-derstanding 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 ap-proaches 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 com-binations. The majority of reviewed studies compared stress proxies calculated from single-source sensor do-mains 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 recom-mend 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.