基于特征波段选择和机器学习的陆地棉叶片水分估算

doi:10.12118/j.issn.1000-6060.2022.667

干旱区地理 ›› 2023, Vol. 46 ›› Issue (11): 1836-1847.doi: 10.12118/j.issn.1000-6060.2022.667

基于特征波段选择和机器学习的陆地棉叶片水分估算

崔锦涛¹(),买买提·沙吾提^1,^2,³()

1.新疆大学地理与遥感科学学院，新疆乌鲁木齐 830046
2.新疆绿洲生态重点实验室，新疆乌鲁木齐 830046
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 830046

收稿日期:2022-12-15 修回日期:2023-01-17 出版日期:2023-11-25 发布日期:2023-12-05
通讯作者: 买买提·沙吾提（1976-），男，博士，副教授，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: korxat@xju.edu.cn
作者简介:崔锦涛（1997-），男，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: 107552101099@stu.xju.edu.cn
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

CUI Jintao¹(),Mamat SAWUT^1,^2,³()

1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China
2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, Xinjiang, China
3. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, Xinjiang, China

Received:2022-12-15 Revised:2023-01-17 Online:2023-11-25 Published:2023-12-05

摘要/Abstract

摘要：

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。

关键词: 光谱, 叶片含水量, 特征波段选择, 机器学习

Abstract:

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

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.

CUI Jintao, Mamat SAWUT. Estimation of leaf water content in upland cotton based on feature band selection and machine learning[J]. Arid Land Geography, 2023, 46(11): 1836-1847.

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数据类型	样本类型	样本数/个	叶片水分含量/%
数据类型	样本类型	样本数/个	均值	标准偏差	变异系数
数据集Ⅰ	建模集	70	78.34	0.043	5.54
	验证集	30	77.30	0.042	5.40
	样本集	100	78.03	0.043	5.50
数据集Ⅱ	验证集	38	79.00	0.074	9.35
数据集Ⅱ	样本集	125	77.79	0.080	10.26

变量筛选方法	描述
CC	运算效率高，过程简单。变量间存在共线性。
CARS	有效去除自相关性高的波段，适合高维数据的筛选^[23]。变量间存在共线性，选择波段稳定性低。
SPA	变量间冗余少，共线性最小，缩短建模时间^[24]。没有考虑所有特征波长之间的共线性^[25];挑选特征变量过程中倾向于选择共线性较小的变量点而不是有效变量点^[25]。
GA	具有全局优化能力。但需要多次运算以确定最佳变量子集。
MC-UVE	稳定性较高。需要定义阈值，导致变量数目改变。
CARS-SPA	进一步剔除冗余信息，提取出有效波段且多重共线性较低，运算效率较高^[6]。筛选变量较少，容易丢失关键信息。特征波长集建模效果受粗选算法结果的影响较大^[26]。

微分阶数	算法	表达式	R²	RMSE
0.7	WOA-RFR	y=0.6934x+0.2403	0.894	0.017
	SVR	y=0.4627x+0.4201	0.553	0.029
	RFR	y=0.5463x+0.3546	0.885	0.021
0.9	WOA-RFR	y=0.7086x+0.2290	0.846	0.018
	SVR	y=0.5242x+0.3706	0.650	0.026
	RFR	y=0.5545x+0.3491	0.882	0.021
1.1	WOA-RFR	y=0.7086x+0.2254	0.877	0.017
	SVR	y=0.6291x+0.2896	0.767	0.022
	RFR	y=0.6121x+0.3045	0.897	0.019
1.3	WOA-RFR	y=0.6778x+0.2522	0.927	0.016
	SVR	y=0.7505x+0.1953	0.881	0.016
	RFR	y=0.6206x+0.2976	0.898	0.019
1.7	WOA-RFR	y=0.6381x+0.2816	0.844	0.020
	SVR	y=0.7293x+0.2117	0.889	0.016
	RFR	y=0.5831x+0.3271	0.893	0.020
1.9	WOA-RFR	y=0.7061x+0.2293	0.851	0.018
	SVR	y=0.6169x+0.3022	0.732	0.023
	RFR	y=0.5676x+0.3392	0.880	0.021

变量筛选方法	算法	建模集		验证集
变量筛选方法	算法	R²	RMSE	R²	RMSE
全波段	WOA-RFR	0.573	0.029	0.480	0.030
	SVR	0.375	0.032	0.035	0.037
	RFR	0.583	0.030	0.021	0.032
CC	WOA-RFR	0.927	0.016	0.946	0.017
	SVR	0.881	0.016	0.908	0.016
	RFR	0.898	0.019	0.950	0.022
CARS	WOA-RFR	0.912	0.017	0.929	0.015
	SVR	0.688	0.040	0.977	0.033
	RFR	0.889	0.025	0.916	0.023
SPA	WOA-RFR	0.937	0.016	0.941	0.015
	SVR	0.398	0.034	0.757	0.024
	RFR	0.913	0.022	0.964	0.022
GA	WOA-RFR	0.622	0.028	0.647	0.025
	SVR	0.712	0.024	0.723	0.002
	RFR	0.889	0.024	0.858	0.001
MC-UVE	WOA-RFR	0.847	0.020	0.868	0.016
	SVR	0.882	0.015	0.883	0.015
	RFR	0.852	0.025	0.821	0.024
CARS-SPA	WOA-RFR	0.935	0.017	0.942	0.019
	SVR	0.326	0.036	0.568	0.029
	RFR	0.911	0.023	0.878	0.024

基于特征波段选择和机器学习的陆地棉叶片水分估算

Estimation of leaf water content in upland cotton based on feature band selection and machine learning

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