收稿日期: 2022-12-15
修回日期: 2023-01-17
网络出版日期: 2023-12-05
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)
Estimation of leaf water content in upland cotton based on feature band selection and machine learning
Received date: 2022-12-15
Revised date: 2023-01-17
Online published: 2023-12-05
棉花叶片含水量的及时准确监测对于评价棉花生长状态具有重要作用。为了精准估算棉花叶片含水量，以新疆渭干河-库车河三角洲绿洲田间尺度上棉花叶片的高光谱数据和叶片水分数据为基础，采用分数阶微分对原始光谱进行处理，通过相关系数分析法、竞争性自适应重加权采样算法（Competitive adaptive reweighted sampling，CARS）、连续投影算法（Successive projections algorithm，SPA）、遗传算法（Genetic algorithm，GA）、蒙特卡罗无信息变量消除算法（Monte Carlo uninformative variables elimination，MC-UVE）以及将CARS与SPA耦合等方法筛选特征波段，采用基于鲸鱼优化算法（Whale optimization algorithm，WOA）改进随机森林回归（Random forest regression，RFR）建立全波段和特征波段的叶片水分含量反演模型，并使用独立样本进行验证分析。结果表明：（1） 不同特征波段筛选方法得到的波段数量与位置不同，其中MC-UVE所得特征波段数量为8个，CARS所得特征波段数量为38个。SPA、GA与CARS-SPA方法中特征波段位置较为一致，基本集中在近红外的950~1050 nm范围内。（2） CARS-SPA-WOA-RFR模型反演效果最好，模型预测值决定系数（R2）=0.93，均方根误差（Root mean square error，RMSE）=0.032。最终构建的模型可为准确快速地监测棉花旱情以及精准灌溉提供决策依据。
崔锦涛 , 买买提·沙吾提 . 基于特征波段选择和机器学习的陆地棉叶片水分估算[J]. 干旱区地理, 2023 , 46(11) : 1836 -1847 . DOI: 10.12118/j.issn.1000-6060.2022.667
It is critical to ensure timely and accurate monitoring of leaf water content (LWC) when assessing the growth status of cotton. To accurately estimate cotton LWC, hyperspectral data, and leaf water data from cotton leaves in the oasis of the Ugan River-Kuqa River Delta, Xinjiang, China, were selected and processed using fractional differentiation of raw spectra. The sample were analyzed through correlation coefficient analysis, competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), genetic algorithm (GA), Monte Carlo uninformative variables elimination (MC-UVE), and a combination of CARS and SPA to filter the feature bands. The modeling of the LWC inversion was executed through random forest regression (RFR) based on the whale optimization algorithm (WOA), and independent samples were used for validation analysis. The results show that: (1) The disparities in the number and positions of the feature bands obtained using the different feature band screening methods are different, where the number of feature bands obtained through MC-UVE is 8 while CARS produced 38. The positions of the characteristic bands identified through the SPA, GA, and CARS-SPA methods are considerably consistent and fundamentally concentrated in the near-infrared range of 950-1050 nm. (2) The CARS-SPA-WOA-RFR model has the best inversion with an R2 of 0.93 and a root mean square error of 0.032. This model can provide a decision basis for accurate and rapid monitoring of cotton drought and precision irrigation.
Key words： spectral; leaf water content; feature band selection; machine learning
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