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Proceedings Paper

Performance of fluorescence retrieval methods and fluorescence spectrum reconstruction under various sensor spectral configurations
Author(s): Rong Li; Feng Zhao
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Paper Abstract

Solar-induced chlorophyll fluorescence is closely related to photosynthesis and can serve as an indicator of plant status. Several methods have been proposed to retrieve fluorescence signal (Fs) either at specific spectral bands or within the whole fluorescence emission region. In this study, we investigated the precision of the fluorescence signal obtained through these methods under various sensor spectral characteristics. Simulated datasets generated by the SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) model with known ‘true’ Fs as well as an experimental dataset are exploited to investigate four commonly used Fs retrieval methods, namely the original Fraunhofer Line Discriminator method (FLD), the 3 bands FLD (3FLD), the improved FLD (iFLD), and the Spectral Fitting Methods (SFMs). Fluorescence Spectrum Reconstruction (FSR) method is also investigated using simulated datasets. The sensor characteristics of spectral resolution (SR) and signal-to-noise ratio (SNR) are taken into account. According to the results, finer SR and SNR both lead to better accuracy. Lowest precision is obtained for the FLD method with strong overestimation. Some improvements are made by the 3FLD method, but it still tends to overestimate. Generally, the iFLD method and the SFMs provide better accuracy. As to FSR, the shape and magnitude of reconstructed Fs are generally consistent with the ‘true’ Fs distributions when fine SR is exploited. With coarser SR, however, though R2 of the retrieved Fs may be high, large bias is likely to be obtained as well.

Paper Details

Date Published: 14 October 2015
PDF: 11 pages
Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96371M (14 October 2015); doi: 10.1117/12.2196429
Show Author Affiliations
Rong Li, BeiHang Univ. (China)
Feng Zhao, BeiHang Univ. (China)

Published in SPIE Proceedings Vol. 9637:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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