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CHINESE JOURNAL OF OIL CROP SCIENCES ›› 2022, Vol. 44 ›› Issue (6): 1320-1328.doi: 10.19802/j.issn.1007-9084.2021290

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Sensitive vegetation indices and optimal bandwidths for monitoring peanut LAI and AGB

Zhong-sheng CAO1(), Yan-da LI1(), Jun-bao HUANG1, Bin-feng SUN1, Chun YE1, Shi-fu SHU1, Luo-fa WU1, Yong-chao TIAN2   

  1. 1.Institute of Agricultural Engineering, Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment, Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
    2.National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2021-11-12 Online:2022-12-25 Published:2022-11-24
  • Contact: Yan-da LI E-mail:czsheng2015@outlook.com;liyanda2008@126.com

Abstract:

To promote the application of spectral remote sensing on rapid nondestructive spectral monitoring for peanut production, sensitive vegetation indices and their optimal bandwidths for estimating peanut leaf area index (LAI) and aboveground biomass (AGB) were investigated. Peanut LAI, AGB and hyperspectral reflectance data, were collected from 2 field experiments encompass variations in 2 years, with 2 cultivars and 4 nitrogen application rates. Sensitive vegetation indices for LAI and AGB were identified and effect of optimal bandwidths on sensitive vegetation indices were analyzed using the in-site dataset. Results showed that the normalized difference red edge (NDRE(λ790, λ720)) was the most sensitive vegetation index for both LAI and AGB. Nevertheless, in the exploration of bandwidth based on data from an independent experiment, the normalized difference red edge (NDRE(λ790-b33,λ720-b53)), which contains the 790 nm central band (λ790) with 33 nm bandwidth (b33) and 720 nm central band (λ720) with 53 nm bandwidth (b53), exhibited greater practicability in LAI estimation with a determination coefficient (R2) of 0.7482 and a relative root mean square error (RRMSE) of 13.88%. The normalized difference red edge (NDRE(λ790-b89, λ720-b89)), which contains the 790 nm central band (λ790) with 89 nm bandwidth (b89) and 720 nm central band (λ720) with 89 nm bandwidth (b89), performed best for monitoring AGB (R2= 0.7103, RRMSE=20.42%). Considering the accuracy and convenience in application, it was demonstrated that NDRE(λ790-b33,λ720-b53) and NDRE(λ790-b89, λ720-b89) could be used to monitor peanut LAI and AGB with estimation models of LAI=0.0296×exp(14.365×NDRE) and AGB= 0.6240×exp(20.222×NDRE), respectively.

Key words: peanut growth vigor, leaf area index, aboveground biomass, vegetation index, bandwidth, model

CLC Number: