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16 September 2017 Detecting and monitoring water stress states in maize crops using spectral ratios obtained in the photosynthetic domain
Gladimir V. G. Baranoski, Spencer R. Van Leeuwen
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Journal of Applied Remote Sensing, Vol. 11, Issue 3, 036025 (September 2017). https://doi.org/10.1117/1.JRS.11.036025
Abstract
The reliable detection and monitoring of changes in the water status of crops composed of plants like maize, a highly adaptable C4 species in large demand for both food and biofuel production, are longstanding remote sensing goals. Existing procedures employed to achieve these goals rely predominantly on the spectral signatures of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. It has been suggested that such procedures could be implemented using subsurface reflectance to transmittance ratios obtained in the visible (photosynthetic) domain with the assistance of polarization devices. However, the experiments leading to this proposition were performed on detached maize leaves, which were not influenced by the whole (living) plant’s adaptation mechanisms to water stress. In this work, we employ predictive simulations of light–leaf interactions in the photosynthetic domain to demonstrate that the living specimens’ physiological responses to dehydration stress should be taken into account in this context. Our findings also indicate that a reflectance to transmittance ratio obtained in the photosynthetic domain at a lower angle of incidence without the use of polarization devices may represent a cost-effective alternative for the assessment of water stress states in maize crops.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Gladimir V. G. Baranoski and Spencer R. Van Leeuwen "Detecting and monitoring water stress states in maize crops using spectral ratios obtained in the photosynthetic domain," Journal of Applied Remote Sensing 11(3), 036025 (16 September 2017). https://doi.org/10.1117/1.JRS.11.036025
Received: 25 May 2017; Accepted: 24 August 2017; Published: 16 September 2017
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Cited by 2 scholarly publications.
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KEYWORDS
Reflectivity
Transmittance
In vivo imaging
Water
Computer simulations
Data modeling
In vitro testing
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