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Smart Agriculture ›› 2021, Vol. 3 ›› Issue (1): 29-39.doi: 10.12133/j.smartag.2021.3.1.202102-SA004

• Topic--Frontier Technology and Application of Agricultural Phenotype • Previous Articles     Next Articles

Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data

SHU Meiyan1(), CHEN Xiangyang2, WANG Xiqing2(), MA Yuntao1()   

  1. 1.College of Land Science and Technology, China Agricultural University, Beijing 100193, China
    2.College of Biological Science, China Agricultural University, Beijing 100193, China
  • Received:2021-01-28 Revised:2021-03-11 Online:2021-03-30 Published:2021-06-01
  • corresponding author: Xiqing WANG,Yuntao MA E-mail:2448858578@qq.com;wangxq21@cau.edu.cn;yuntao.ma@cau.edu.cn

Abstract:

In order to assess maize growth status accurately and quickly for improving maize precise management, field experiment was conducted in Gongzhuling research station, Jilin Academy of Agricultural Sciences, Jilin province. Experimental design included 3 planting densities and 5 maize materials. The near-ground hyperspectral data and the unmanned aerial vehicle (UAV) hyperspectral images were obtained when maize were during V11-V12 stage. The application abilities of the hyperspectral data obtained from the two phenotyping platforms were compared and analyzed in the estimation of maize leaf area index (LAI) and aboveground biomass. In this study, 21 commonly used spectral vegetation indices were constructed based on ground hyperspectral data, and then the estimation models of maize LAI and aboveground biomass were established based on ground hyperspectral full-bands, UAV hyperspectral full-bands and vegetation indices and partial least square regression method, respectively. According to the variance estimation of regression coefficients, the important bands of LAI and aboveground biomass were selected, and the partial least square method was also used to establish the estimation model of maize LAI and aboveground biomass based on important bands. The results showed that the canopy spectral reflectance of the same maize material increased with the increase of planting density in the near infrared bands. Among the 5 maize materials under the same planting density, the canopy spectral reflectance of wild type material was the lowest in the visible and near infrared bands. For LAI, the model constructed based on vegetation indices had the best estimation result, with R2, RMSE and rRMSE values of 0.70, 0.92 and 15.94%. For aboveground biomass, the model constructed based on the sensitive spectral bands (839-893 nm and 1336-1348 nm) had the best estimation results, with R2, RMSE and rRMSE values of 0.71, 12.31 g and 15.89%, which showed that there was information redundancy in hyperspectral bands in the estimation of aboveground biomass, and the estimation accuracy could be improved by reducing the number of spectral bands and selecting sensitive spectral bands. In summary, the UAV hyperspectral images have a good application ability in the estimation of maize LAI and aboveground biomass, and can quickly and effectively extract the parameters information of maize growth. For specific parameters, sensitive spectral bands selected can provide reliable basis for the development and practical application of multi-spectrum in the future. The study can provide a reference for the use of hyperspectral technology in the management of precision agriculture at the community scale.

Key words: hyper-spectrum, maize, leaf area index, aboveground biomass, partial least squares regression, UAV remote sensing

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