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中国农学通报 ›› 2023, Vol. 39 ›› Issue (1): 123-132.doi: 10.11924/j.issn.1000-6850.casb2021-1212

• 农业信息·科技教育 • 上一篇    下一篇

基于高光谱的棉花叶片磷含量估测模型

马元花1,2(), 印彩霞1, 王红玉1, 刘宇轩1, 刘前通1, 张泽1()   

  1. 1.石河子大学农学院/新疆生产建设兵团绿洲生态农业重点实验室,新疆石河子 832000
    2.西北农林科技大学农学院,陕西杨凌 712100
  • 收稿日期:2021-12-28 修回日期:2022-11-10 出版日期:2023-01-05 发布日期:2022-12-27
  • 通讯作者: 张泽
  • 作者简介:马元花,女,1999年出生,新疆人,硕士,研究方向为作物信息技术与精准栽培。通信地址:712100 陕西杨凌邰城路3号 西北农林科技大学农学院,E-mail:1162392907@qq.com
  • 基金资助:
    国家大学生创新创业训练项目“棉花磷素垂直分布特征与敏感光谱指数模型构建研究”(202010759004);石河子大学学科交叉研究计划项目“基于机器学习的棉花叶片磷素遥感反演模型建立研究”(2021DX0102)

Estimation Model of Cotton Leaf Phosphorus Content Based on Hyperspectral Reflectance

MA Yuanhua1,2(), YIN Caixia1, WANG Hongyu1, LIU Yuxuan1, LIU Qiantong1, ZHANG Ze1()   

  1. 1. Agricultural College of Shihezi University/ Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi, Xinjiang 832000
    2. College of Agronomy, Northwest A & F University, Yangling, Shaanxi 712100
  • Received:2021-12-28 Revised:2022-11-10 Online:2023-01-05 Published:2022-12-27
  • Contact: ZHANG Ze

摘要:

研究不同施磷条件下棉花叶片叶绿素含量的变化规律,旨在建立基于高光谱的叶片磷含量估测模型,实现棉花叶片磷含量快速监测。在盆栽试验条件下,设置不同的磷肥量,测定棉花功能叶叶绿素含量与磷含量,并利用植被指数和叶绿素含量的相关性构建磷含量的光谱变量,从而实现利用高光谱对棉花叶片磷含量的定量监测。结果表明:(1)棉花播种后100天左右,叶片磷含量与叶绿素呈现显著关系(决定系数R2=0.96)。(2)利用多个植被指数(X)和叶绿素含量(I)的相关性构建倒一叶、倒二叶、倒三叶、倒四叶的磷含量光谱变量,其中各叶片相关性最优的模型:倒一叶(L1)为I1=2.6131XRENDVI-0.4275,XRENDV为红边归一化植被指数,R2=0.71,RMSE=0.2;倒二叶(L2)为I5=0.0142XTVI+0.3274,XTVI为三角植被指数,R2=0.76,RMSE=0.09;倒三叶(L3)为I9=-0.3445XARI+0.4996,XARI为花青素反射指数,R2=0.47,RMSE=0.15;倒四叶(L4)为I10=-0.5082XARI+0.3484,R2=0.82,RMSE=0.10。(3)构建基于磷含量光谱变量的高光谱棉花叶片磷含量反演模型,模型R2RMSE分别达0.75、0.04。因此,基于不同叶位的叶片植被指数和叶绿素含量的相关性,以及叶绿素含量和叶片磷含量的线性关系,可以实现高光谱对棉花叶片磷含量的有效估测。

关键词: 棉花, 高光谱, 叶位, 叶绿素, 叶片磷含量

Abstract:

The changes of chlorophyll content in cotton leaves under different phosphorus application rates were studied, aiming to establish an estimation model of leaf phosphorus content based on hyperspectrum to realize the rapid monitoring of phosphorus content in cotton leaves. Pot experiments were conducted, the chlorophyll content and phosphorus content of cotton functional leaves were determined under different phosphorus application rates, and the spectral variables of phosphorus content were constructed by using the correlation between vegetation index and chlorophyll content, thus achieving the quantitative monitoring of phosphorus content in cotton leaves by using hyperspectrum. The following results were obtained. (1) About 100 days after sowing, leaf phosphorus content and chlorophyll content has a significant relationship (R2=0.96). (2) The spectral variables of phosphorus content in the first leaf, the second leaf, the third leaf and the fourth leaf from the top are constructed by the correlation between vegetation index (X) and chlorophyll content (I). Each blade of the optimal model is shown as follows: for the first leaf from the top (L1): I1=2.6131XRENDVI-0.4275, the XRENDVI represents red edge normalized difference vegetation index, R2 and root mean square error (RMSE) reach 0.71 and 0.20, respectively; for the second leaf from the top (L2): I5 =0.0142XTVI+0.3274, the XTVI represents triangular vegetation index, R2 and RMSE reach 0.76 and 0.09, respectively; for the third leaf from the top (L3): I9=-0.3445XARI+0.4996, the XARI represents anthocyanin reflectance index, R2 and RMSE reach 0.47 and 0.15, respectively; and for the fourth leaf from the top (L4): I10=-0.5082XARI+0.3484, R2 and RMSE reach 0.82 and 0.10, respectively. (3) The inversion model of leaf phosphorus content of cotton is constructed based on spectral variables, and the R2 and RMSE reach 0.75 and 0.04, respectively. Therefore, the leaf phosphorus content of cotton can be effectively monitored based on the correlation between leaf vegetation index and chlorophyll content at different leaf positions, and the liner relationship between chlorophyll content and leaf phosphorus content.

Key words: cotton, hyperspectrum, leaf position, chlorophyll, leaf phosphorus content