基于多植被指数组合的棉花叶片叶绿素含量估算

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

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])、Mndvi_(705)、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R~2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。

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]),Mndvi_(705),ND,VOG1,NVI,TVI,VOG2 combined vegetation indices.The model exhibited R^(2) 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.