干旱区研究

干旱区研究 >

2023 , Vol. 40 >Issue 11: 1865 - 1874

DOI: https://doi.org/10.13866/j.azr.2023.11.16

农业生态

基于多植被指数组合的棉花叶片叶绿素含量估算

  • 阿热孜古力·肉孜 ,
  • 买买提·沙吾提 ,
  • 何旭刚 ,
  • 冶晓文
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  • 1.新疆大学地理与遥感科学学院，新疆 乌鲁木齐 830017
    2.新疆绿洲生态重点实验室，新疆 乌鲁木齐 830017
    3.智慧城市与环境建模自治区普通高校重点实验室，新疆 乌鲁木齐 830017
阿热孜古力·肉孜（1997-），女，硕士研究生，主要从事干旱区资源环境及农业遥感应用方面的研究. E-mail: 173762855@qq.com

收稿日期: 2023-05-11

  修回日期: 2023-07-20

  网络出版日期: 2023-12-01

基金资助

新疆自然科学计划（自然科学基金）联合基金项目(2021D01C055)

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Estimation of cotton leaf chlorophyll content based on combinations of multi-vegetation indices

  • ROZI Areziguli ,
  • SAWUT Mamat ,
  • Xugang HE ,
  • Xiaowen YE
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  • 1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, Xinjiang, China
    2. Xinjiang Key Laboratory of Oasis Ecology, Urumqi 830017, Xinjiang, China
    3. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Urumqi 830017, Xinjiang, China

Received date: 2023-05-11

  Revised date: 2023-07-20

  Online published: 2023-12-01

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摘要

叶绿素含量是表征植被生长状况的重要参考指标，利用高光谱技术快速，精确地监测棉花叶片叶绿素含量，以新疆125个苗期棉花叶片样本为研究对象，通过测定其叶绿素含量与光谱数据，采用多种光谱预处理和多植被指数相结合的方法，构建了WOA-RFR棉花叶片叶绿素含量定量反演模型，并与SVR和RFR模型结果进行对比分析。结果表明：（1） 光谱变换方法中对数变换、分数阶微分和连续小波变换均能有效地提高植被指数与叶绿素含量的相关性。（2） 基于分数阶微分0.9阶变换的Vogelmann3、RVI、DVI、SR[675-700]、Mndvi705、ND、VOG1、NVI、TVI和VOG2植被指数组合的WOA-RFR模型反演效果最佳，其建模集和验证集模型R2分别为0.920和0.955，RMSE分别为0.987和0.986，MRE分别为0.013和0.014，与RFR和SVR模型相比，预测精度有所提高，WOA算法优化模型效果明显。研究结果可为棉花叶片叶绿素含量定量反演提供决策依据。

关键词： 植被指数组合; 棉花; 叶绿素含量; 鲸鱼优化算法

本文引用格式

阿热孜古力·肉孜 , 买买提·沙吾提 , 何旭刚 , 冶晓文 . 基于多植被指数组合的棉花叶片叶绿素含量估算[J]. 干旱区研究, 2023 , 40(11) : 1865 -1874 . DOI: 10.13866/j.azr.2023.11.16

Abstract

Chlorophyll content is a crucial indicator for characterizing vegetation growth. In this study, we utilized high-spectral technology to rapidly monitor the chlorophyll contents of cotton leaves. We collected 125 cotton leaf seedling samples from Xinjiang and measured their chlorophyll content and spectral data. To achieve this, we employed various spectral preprocessing techniques and used a combination of vegetation indices. Subsequently, we constructed a whale optimization algorithm/random forest regression (WOA-RFR) quantitative inversion model for cotton leaf chlorophyll content. Finally, we conducted a comparative analysis, contrasting the results of the WOA-RFR model with those obtained from the support vector regression (SVR) and RFR models. The results indicated that the spectral transformation methods (logarithm transformation, fractional order differentiation, and wavelet transformation) effectively improved the correlation between the vegetation indices and the chlorophyll content. We also found that the best inversion performance was achieved with the WOA-RFR model using a fractional order differentiation with a transformation order of 0.9 and the Vogelmann3, RVI, DVI, SR[675-700], Mndvi705, ND, VOG1, NVI, TVI, VOG2 combined vegetation indices. The model exhibited R2 values of 0.920 and 0.955 for the training set and validation set, respectively. The corresponding RMSE values were 0.987 and 0.986, while the MRE values were 0.013 and 0.014. Compared to the RFR and SVR models, the WOA-RFR model demonstrated higher predictive accuracy, and the optimization effect of the WOA algorithm was evident. As a result, this study provides valuable decision-making support for accurately quantifying cotton leaf chlorophyll content.

Key words： combination of vegetation index; cotton; chlorophyll content; whale optimization algorithm

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