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

• 专题--作物表型前沿技术与应用 • 上一篇    下一篇

基于高光谱数据的玉米叶面积指数和生物量评估

束美艳1(), 陈向阳2, 王喜庆2(), 马韫韬1()   

  1. 1.中国农业大学 土地科学与技术学院,北京 100193
    2.中国农业大学 生物学院,北京 100193
  • 收稿日期:2021-01-28 修回日期:2021-03-11 出版日期:2021-03-30
  • 基金资助:
    国家重点研发计划(2016YFD0300202);内蒙古自治区科技重大专项(2019ZD024)
  • 作者简介:束美艳(1993-),女,博士研究生,研究方向为数字农业。E-mail:2448858578@qq.com
  • 通信作者:

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
  • Supported by:
    National Key Research and Development Program of China(2016YFD0300202);Inner Mongolia Autonomous Region Science and Technology Major Project (2019ZD024)

摘要:

利用高光谱技术获取玉米农学参数信息,有助于提升玉米精准管理水平。本研究基于3个种植密度和5份玉米材料的田间试验,获取玉米大喇叭口期的地面ASD高光谱数据与无人机高光谱影像,分析不同种植密度下不同遗传材料的叶面积指数(LAI)和单株地上部生物量,构建基于全波段、敏感波段和植被指数的LAI和单株地上部生物量高光谱估算模型,比较分析两类高光谱数据在玉米表型性状参数上的监测能力。结果表明,野生型玉米材料的冠层光谱反射率在近红外波段随着种植密度的增大而增大;同一种植密度下的野生型玉米材料的光谱反射率在可见光和近红外波段均为最低。在可见光波段550 nm的波峰处,4种转基因材料的光谱反射率比野生型玉米材料的光谱反射率提高4.52%~19.9%,在近红外波段870 nm的波峰处,4种转基因材料的光谱反射率比野生型玉米材料的光谱反射率提高23.64%~57.05%。基于21个高光谱植被指数构建的模型对LAI的估算效果最好,测试集决定系数R2为0.70,均方根误差RMSE为0.92,相对均方根误差rRMSE为15.94%。敏感波段反射率(839~893 nm和1336~1348 nm)对玉米单株地上部生物量估算效果最佳,测试集R2为0.71,RMSE为12.31 g,rRMSE为15.89%。综上,田间非成像高光谱和无人机成像高光谱在玉米LAI及生物量估算方面具有较好的一致性,能够快速有效地提取地块尺度玉米农学参数信息,本研究可为高光谱技术在小区尺度的精准农业管理应用提供参考。

关键词: 高光谱, 玉米, 叶面积指数, 地上部生物量, 偏最小二乘回归, 无人机遥感

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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