The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement

Huijie Zhao; Bolun Cui; Guorui Jia; Fuping Gan

doi:10.1117/12.2547342

12 March 2020 The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement

Huijie Zhao, Bolun Cui, Guorui Jia, Fuping Gan

Author Affiliations +

Proceedings Volume 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 114380N (2020) https://doi.org/10.1117/12.2547342
Event: 2019 International Conference on Optical Instruments and Technology, 2019, Beijing, China

Abstract

The atmosphere calibrated airborne and space borne hyperspectral images are the HDRF of canopy. The spatial nonuniformity of HDRF may result in inversion errors of the heavy metal stressing. In this paper, the HDRF of copper stressed plant samples under different illumination conditions was acquired with the laboratory hyperspectral simulation system called MHRS2F. The difference between the HDRF of canopy and the BCRF of leaves was firstly discussed. Then the changes of spatial distribution of the HDRF for different copper concentrations and illumination conditions were discussed. At last, the sensitivity of various vegetation indices to illumination and observation directions was compared. By comparing the prediction accuracy of different vegetation indices on different observation directions and illumination conditions, the HVI and mRENDVI were found to be more stable and accurate.

Citation Download Citation

Huijie Zhao, Bolun Cui, Guorui Jia, and Fuping Gan "The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement", Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 114380N (12 March 2020); https://doi.org/10.1117/12.2547342

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