5 May 2016 On the detection and monitoring of reduced water content in plants using spectral responses in the visible domain
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The water status of cultivated plants can have a significant impact not only on food production, but also on the appropriate usage of increasingly scarce freshwater supplies. Accordingly, the cost-effective detection and monitoring of changes in their water content are longstanding remote sensing goals. Existing procedures employed to achieve these goals are largely based on the spectral responses of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. Recently, it has been suggested that such procedures could be implemented using spectral responses, more specifically spectral subsurface reflectance to transmittance ratios, obtained in the visible domain. The basis for this proposition resides on the premise that a reduced water content (RWC) can result in histological changes whose effects on the foliar optical properties may not be limited to the infrared domain. However, the experiments leading to this proposition were performed on detached leaves, which were not influenced by the whole plant’s adaptation mechanisms to water stress. In this work, we investigate whether the spectral responses of living plant leaves in the visible domain can lead to reliable RWC estimations. We employ measured biophysical data and predictive light transport simulations in order to extend qualitatively and quantitatively the scope of previous studies in this area. Our findings indicate that the living specimens’ physiological responses to water stress should be taken into account in the design of new procedures for the cost-effective RWC estimation using visible subsurface reflectance to transmittance ratios.
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Gladimir V. G. Baranoski, Gladimir V. G. Baranoski, Spencer Van Leeuwen, Spencer Van Leeuwen, Tenn F. Chen, Tenn F. Chen, "On the detection and monitoring of reduced water content in plants using spectral responses in the visible domain", Proc. SPIE 9877, Land Surface and Cryosphere Remote Sensing III, 98770N (5 May 2016); doi: 10.1117/12.2223758; https://doi.org/10.1117/12.2223758

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