15 September 2011 Spectral bio-indicator simulations for tracking photosynthetic activities in a corn field
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Accurate assessment of vegetation canopy optical properties plays a critical role in monitoring natural and managed ecosystems under environmental changes. In this context, radiative transfer (RT) models simulating vegetation canopy reflectance have been demonstrated to be a powerful tool for understanding and estimating spectral bio-indicators. In this study, two narrow band spectroradiometers were utilized to acquire observations over corn canopies for two summers. These in situ spectral data were then used to validate a two-layer Markov chain-based canopy reflectance model for simulating the Photochemical Reflectance Index (PRI), which has been widely used in recent vegetation photosynthetic light use efficiency (LUE) studies. The in situ PRI derived from narrow band hyperspectral reflectance exhibited clear responses to: 1) viewing geometry which affects the light environment; and 2) seasonal variation corresponding to the growth stage. The RT model (ACRM) successfully simulated the responses to the viewing geometry. The best simulations were obtained when the model was set to run in the two layer mode using the sunlit leaves as the upper layer and shaded leaves as the lower layer. Simulated PRI values yielded much better correlations to in situ observations when the cornfield was dominated by green foliage during the early growth, vegetative and reproductive stages (r = 0.78 to 0.86) than in the later senescent stage (r = 0.65). Further sensitivity analyses were conducted to show the important influences of leaf area index (LAI) and the sunlit/shaded ratio on PRI observations.
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Yen-Ben Cheng, Yen-Ben Cheng, Elizabeth M. Middleton, Elizabeth M. Middleton, K. Fred Huemmrich, K. Fred Huemmrich, Qingyuan Zhang, Qingyuan Zhang, Lawrence Corp, Lawrence Corp, Petya Campbell, Petya Campbell, William Kustas, William Kustas, "Spectral bio-indicator simulations for tracking photosynthetic activities in a corn field", Proc. SPIE 8156, Remote Sensing and Modeling of Ecosystems for Sustainability VIII, 815607 (15 September 2011); doi: 10.1117/12.892333; https://doi.org/10.1117/12.892333

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