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Recent Advances in Remote Sensing of Plant Stress

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 3848

Special Issue Editors

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Guest Editor
Senior Research Associate, Luxembourg Institute of Science and Technology (LIST), 5, Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
Interests: hyperspectral and thermal remote sensing; retrieval of biochemical and structural vegetation properties; water stress detection; crop nitrogen assessment
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Guest Editor
Department of Aeronautics and Astronautics, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan
Interests: plant eco-physiology; remote sensing; micrometeorology; agro-ecosystems; precision agriculture; GIS; vegetation and water resources
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Environmental Remote Sensing and Geoinformatics Department, University of Trier, 54286 Trier, Germany
Interests: imaging spectroscopy; thermal remote sensing; geostatistics; data mining

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Guest Editor
Faculty for Geo-Information Science and Earth Observation (ITC), University of Twente, 7500AA Enschede, The Netherlands
Interests: spatial ecology; fragmentation; climate change; hyperspectral remote sensing; image processing; geo-information techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our ability to assess the health of plants is being raised to new levels with the advent of new sensors that can see across a wider range of wavelengths than our eyes are able to. Different types of stresses, such as limitations and excesses of the main abiotic factors light, temperature, water, and nutrients, as well as other stress factors (e.g., pests, pathogens, or pollutants) may cause numerous and complex plant responses (e.g., stomatal closure, photosynthetic inhibition, altered pigment and water contents, etc.). These responses may be revealed by the latest state-of-the-art remote sensing techniques from UAV, manned airborne, and (future) satellite platforms in the solar-reflective (VNIR/SWIR) hyperspectral, thermal infrared (TIR), multi-/hyperspectral, and sun-induced fluorescence (SIF) domains. The ability to reliably detect plant stress using remote sensing has many applications ranging from precision agriculture, industrial pollution, and natural vegetation assessment, including scientific contributions to plant, biogeochemical, and climate change sciences.

This Special Issue aims to highlight advances in the detection and mapping of plant stress using the latest remote sensing techniques. Topics may include, but are not limited, to the following aspects:

  • The detection, mapping, or monitoring of one or several abiotic or biotic stresses
  • Remote sensing from drone, aircraft, or satellite
  • The use of solar-reflective or thermal infrared, multi-/hyperspectral, or sun-induced fluorescence sensors, or the synergistic use of multiple sensors
  • The use of novel semi-empirical (e.g., vegetation indices), physically-based, or statistical approaches

Dr. Martin Schlerf 
Dr. Yoshio Inoue
Prof. Dr. Thomas Udelhoven
Prof. Dr. Andrew Skidmore
Dr. Jochem Verrelst
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • biotic and abiotic stresses
  • water/drought stress
  • nutrient limitation
  • ecophysiological functioning
  • hyperspectral
  • thermal infrared
  • sun-induced fluorescence
  • sensor synergies
  • unmanned aerial vehicles
  • photochemical reflectance index

Published Papers (1 paper)

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20 pages, 5514 KiB  
Canopy Nitrogen Concentration Monitoring Techniques of Summer Corn Based on Canopy Spectral Information
by Lu Liu, Zhigong Peng, Baozhong Zhang, Zheng Wei, Nana Han, Shaozhe Lin, He Chen and Jiabing Cai
Sensors 2019, 19(19), 4123; - 23 Sep 2019
Cited by 8 | Viewed by 2922
Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a [...] Read more.
Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a lysimeter experiment was carried out to examine the characteristics of canopy spectral reflectance variation of summer corn under different fertilization levels. The relationship between canopy spectral reflectance and nitrogen concentration was investigated, based on which sensitive bands for the corn canopy nitrogen monitoring were selected and a suitable spectral index model was determined. The results suggest that under different fertilization levels, the canopy spectral reflectance of summer corn decreases with the increase of the canopy nitrogen concentration in the visible light band, but varies in the opposite direction in the near-infrared band, with a premium put on a higher correlation between the spectral reflectance of the characteristic bands and their first derivatives and the canopy nitrogen concentration. The most sensitive bands for monitoring the canopy nitrogen concentration using spectral reflectance and its first derivative are found to be 762 nm and 726 nm and the correlation coefficients are 0.550 and 0.795, respectively. The optimal band combination, generated by multivariate stepwise regression analysis, is composed of 762 nm, 944 nm and 957 nm bands. From the 55 reported spectral index models of crop nitrogen concentration monitoring, the most suitable index model, NDRE, is chosen such that this index model has the highest correlation with the canopy nitrogen concentration in summer corn. This model has a significant positive correlation with the canopy nitrogen concentration at each growth period, and the correlation coefficient is up to 0.738 during the whole growth period. Spectral monitoring models of canopy nitrogen concentration are constructed using sensitive bands, and a combination of bands and the spectral index, suggesting that these models perform well in monitoring. The models arranged in descending order of simulation accuracy are as follows: the suitable spectral index model, the optimal band combination model, the sensitive band reflectance first derivative model, the sensitive band reflectance model. The determination coefficients are 0.754, 0.711, 0.639 and 0.306, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Plant Stress)
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