22 July 2019 Predicting leaf chlorophyll content and its nonuniform vertical distribution of summer maize by using a radiation transfer model
Xiaobin Xu, Zhenhai Li, Xiaodong Yang, Guijun Yang, Cong Teng, Hongchun Zhu, Shuaibing Liu
Author Affiliations +
Abstract

Remote sensing technology is an effective method of monitoring chlorophyll content, an important parameter for vegetation health. The chlorophyll information based on spectral information needs to consider the vertical characteristics of plants. Hyperspectral features [spectral reflectance (SR), spectral indices, and wavelet coefficients (WC)] were first selected to construct the cost function in the PROSPECT model-optimized inversion to improve the accuracy and efficiency of leaf chlorophyll content (LCC) inversion. Second, the sensitivity of LCC to leaf SR, vegetation index (VI), and WC were analyzed. Finally, LCC was inverted by the PROSPECT model using the iterative inversion algorithm, and the chlorophyll content of the vertical profile in maize was monitored. The following results were obtained. (1) According to the extended Fourier amplitude sensitivity test (EFAST) used to construct the cost-function inversion of LCC, the normalized difference vegetation index canste (NDVIcanste) and WC based on EFAST method which was used to construct the cost-function inversion of LCC, yielded higher accuracy than other spectral features. These two methods, combined with sensitivity analysis can provide accurate inversion results by weakening the influence of other parameters on spectral changes and eliminating the interference information between bands. (2) The cost function based on NDVIcanste in the LCC optimization exhibited overestimation, whereas WC can solve the problem efficiently. The continuous wavelet transform can extract weak information among the spectra, whereas the other two methods of SR and VI cannot easily obtain this information. (3) The vertical distribution of LCC in different maize varieties and treatments is in accordance with the parabola law, but the chlorophyll content of the different leaf positions on the vertical profile is somewhat different. The results reflect the vertical distribution of chlorophyll content through the radiation transfer of leaves, thus providing a theoretical basis for their further combination.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Xiaobin Xu, Zhenhai Li, Xiaodong Yang, Guijun Yang, Cong Teng, Hongchun Zhu, and Shuaibing Liu "Predicting leaf chlorophyll content and its nonuniform vertical distribution of summer maize by using a radiation transfer model," Journal of Applied Remote Sensing 13(3), 034505 (22 July 2019). https://doi.org/10.1117/1.JRS.13.034505
Received: 19 January 2019; Accepted: 3 July 2019; Published: 22 July 2019
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Cited by 7 scholarly publications.
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KEYWORDS
Continuous wavelet transforms

Reflectivity

Remote sensing

Vegetation

Nitrogen

Absorption

Wavelets

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