Optimal Algorithm of Red Edge Position for Soybean Leaf Under CO2 Stress

Abstract

Abstract:

Red edge parameters are widely used to invert vegetation parameters in quantitative remote sense. The red edge position, as a very sensitive indicator for monitoring vegetation stress, is strongly correlated with vegetation biochemical components. In order to obtain the best red edge position algorithm, six red edge position extraction methods, which are red edge position maximum first derivative method, Lagrange method, line extrapolate method, four-point interpolation method, Gaussian method and polynomial fitting method, were compared for soybean leaf under higher CO2 stress. The results show that the different algorithms of red edge position are significantly linear correlation with chlorophyll content of soybean leaf. However, largest first derivative method and Lagrange method are the optimal extract methods to calculate red edge position for soybean leaf under CO2 stress. Moreover, the maximum first derivative method is more simple and stable. The results imply that red edge position changes can reflect plant chlorophyll content and can be used to monitor CO2 leakage during CCS project using aboveground plant remote sensing data.

Key words: different algorithm, red edge position, CO2 monitoring, spectral characteristics of soybean

CLC Number:

S126

HAO Ruijuan, WANG Zhoufeng, WANG Wenke, ZHAO Yaqian. Optimal Algorithm of Red Edge Position for Soybean Leaf Under CO2 Stress[J]. Journal of Atmospheric and Environmental Optics, 2016, 11(1): 51-60.

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