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作物学报 ›› 2023, Vol. 49 ›› Issue (12): 3364-3376.doi: 10.3724/SP.J.1006.2023.33001

• 耕作栽培·生理生化 • 上一篇    下一篇

基于无人机多光谱影像和机器学习方法的玉米叶面积指数反演研究

马俊伟1,2(), 陈鹏飞2,4,*(), 孙毅3, 谷健3, 王李娟1,*()   

  1. 1江苏师范大学地理测绘与城乡规划学院, 江苏徐州 221116
    2中国科学院地理科学与资源研究所 / 资源与环境信息系统国家重点实验室, 北京 100101
    3中国科学院沈阳应用生态研究所, 辽宁沈阳 110016
    4江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023
  • 收稿日期:2023-01-01 接受日期:2023-04-17 出版日期:2023-12-12 网络出版日期:2023-05-05
  • 通讯作者: * 陈鹏飞, E-mail: pengfeichen@igsnrr.ac.cn; 王李娟, E-mail: wanglj2013@jsnu.edu.cn
  • 作者简介:E-mail: majunwei@jsnu.edu.cn
  • 基金资助:
    中国科学院先导A专项(XDA28040502);国家自然科学基金项目(41871344);江苏师范大学研究生科研创新计划项目(2022XKT0070)

Comparing different machine learning methods for maize leaf area index (LAI) prediction using multispectral image from unmanned aerial vehicle (UAV)

MA Jun-Wei1,2(), CHEN Peng-Fei2,4,*(), SUN Yi3, GU Jian3, WANG Li-Juan1,*()   

  1. 1School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China
    2State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    3Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China
    4Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China
  • Received:2023-01-01 Accepted:2023-04-17 Published:2023-12-12 Published online:2023-05-05
  • Contact: * E-mail: pengfeichen@igsnrr.ac.cn; E-mail: wanglj2013@jsnu.edu.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28040502);National Natural Science Foundation of China(41871344);Jiangsu Normal University Graduate Research Innovation Program Project(2022XKT0070)

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摘要：

为实现基于机器学习方法和无人机影像的叶面积指数(leaf area index, LAI)准确估测。本研究对比了人工神经网络法(Artificial Neural Network algorithm, ANN)、高斯过程回归法(Gaussian Process Regression algorithm, GPR)、支持向量回归法(Support Vector Regression algorithm, SVR)和梯度提升决策树法(Gradient Boosting Decision Tree, GBDT)等几种主流的机器学习方法在基于无人机影像的玉米LAI反演中的优劣。为此, 开展了不同有机肥、无机肥、秸秆还田以及种植密度处理的玉米田间试验, 在不同生育期获取了无人机多光谱影像和LAI数据。基于这些数据, 首先通过相关性分析, 选择对LAI敏感的光谱指数作为估测变量, 然后分别耦合偏最小二乘法(Partial Least Squares Regression, PLSR)和ANN、GPR、SVR、GBDT建立LAI反演模型, 并对它们进行对比分析。结果表明, PLSR+GBDT法构建的LAI反演模型精度最高, 稳定性最好, 建模Rcal2和RMSEcal为0.90和0.25, 验证Rval2和RMSEval为0.90和0.29; 与PLSR+GBDT模型结果最接近的是基于PLSR+GPR法建立的模型, 其建模Rcal2和RMSEcal为0.86和0.30, 验证Rval2和RMSEval为0.89和0.29, 且具有训练速度快, 并能给出反演结果不确定度的优势; PLSR+ANN法的建模Rcal2和RMSEcal为0.85和0.31, 验证Rval2和RMSEval为0.89和0.30; PLSR+SVR法的建模Rcal2和RMSEcal为0.86和0.32, 验证Rval2和RMSEval为0.90和0.33。因此, PLSR+GBDT法和PLSR+GPR法被推荐作为玉米LAI反演模型构建的最优方法。

关键词: 叶面积指数, 机器学习, 无人机, 多光谱影像, 玉米

Abstract:

To make an accurate estimation of leaf are index (LAI) based on machine learning methods and images from UAV, we compared the several mainstream machine learning methods for maize LAI prediction, such as Artificial Neural Network method (ANN), Gaussian Process Regression method (GPR), Support Vector Regression method (SVR), and Gradient Boosting Decision Tree (GBDT). For this purpose, field experiments that considering apply of different amount of organic fertilizer, different amount of inorganic fertilizer, different amount of crop residue, and different planting density were carried out. Based on these experiments, field campaign were conducted to obtain UAV multispectral images and LAI data at different growth stages in maize. Based on above data, firstly, correlation analysis was used to select LAI-sensitive spectral indices, and then the Partial Least Squares Regression method (PLSR) and ANN, GPR, SVR, GBDT were coupled to design the LAI prediction models, respectively, and their performance for LAI prediction were compared. The results showed that the LAI prediction model constructed by PLSR+GBDT method had the highest accuracy and the best stability. The models of R2 and RMSE values were 0.90 and 0.25, and the verified R2 and RMSE values were 0.90 and 0.29 during validation, respectively. The model based on PLSR+GPR model was followed, with R2 and RMSE values of 0.86 and 0.30 during calibration, and R2 and RMSE values of 0.89 and 0.29 during validation, respectively. Besides, it had faster training speed and could give the uncertainty of the prediction. The model designed by PLSR+ANN method had R2 and RMSE values of 0.85 and 0.31 during calibration, and R2 and RMSE values of 0.89 and 0.30 during validation, respectively. The model designed by PLSR+SVR method had R2 and RMSE values of 0.86 and 0.32, and R2 and RMSE values of 0.90 and 0.33, respectively. Therefore, PLSR+GBDT method and PLSR+GPR method are recommended as the optimal methods for designing maize LAI prediction models.

Key words: LAI, machine learning, UAV, multispectral image, maize

图1

研究区位置及田间小区分布情况"

表1

Altum多光谱相机的可见-近红外谱区波段相关参数信息"

波段名称
Band name		 中心波长
Central wavelength		 波宽
Bandwidth
蓝光波段Blue band 475 20
绿光波段Green band 560 20
红光波段Red band 668 10
红边波段Red-edge band 717 10
近红外波段Near infrared band 840 40

表2

本研究选取的光谱指数"

缩写
Abbreviation		 全称
Full name		 公式
Formula		 来源
Source
NDVI 归一化植被指数
Normalized Difference Vegetation Index		 $\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+\text{R} \right)$ [22]
RVI 比值植被指数
Ratio Vegetation Index		 $\text{NIR}/\text{R}$ [23]
DVI 差值植被指数
Difference Environmental Vegetation Index		 $\text{NIR}-\text{R}$ [24]
EVI 增强植被指数
Enhanced Vegetation Index		 $2.5\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+6\text{R}-\text{7}\text{.5B}+\text{1} \right)$ [25]
GNDVI 绿色归一化植被指数
Green Normalized Difference Vegetation Index		 $\left( \text{NIR}-\text{G} \right)/\left( \text{NIR}+\text{G} \right)$ [26]
MSAVI 调整型土壤调节植被指数
Modified Soil Adjusted Vegetation Index		 $\left( \text{2NIR}+1-\text{sqrt}\left( {{\left( 2\text{NIR+1} \right)}^{2}}-\text{8}\left( \text{NIR}-\text{R} \right) \right) \right)/\text{2}$ [27]
OSAVI 优化型土壤调节植被指数
Optimized Soil Adjusted Vegetation Index		 $\text{1}\text{.16}\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+\text{R}+\text{0}\text{.16} \right)$ [28]
TVI 三角形植被指数
Triangular Vegetation Index		 $\text{60}\left( \text{NIR}-\text{G} \right)-\text{100}\left( \text{R}-\text{G} \right)$ [29]
GRVI 绿色比值植被指数
Green Ratio Vegetation Index		 $\text{NIR}/\text{G}-\text{1}$ [30]
SAVI 土壤调节植被指数
Soil Adjusted Vegetation Index		 $\text{1}\text{.5}\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+\text{R}+\text{0}\text{.5} \right)$ [31]
RENDVI 红边归一化差值植被指数
Red Edge Normalized Difference Vegetation Index		 $\left( \text{RE}-\text{R} \right)/\left( \text{RE}+\text{R} \right)$ [32]
RESR 红边比值植被指数
Red-Edge Simple Ratio		 RE/R [33]
MCARI 改进叶绿素吸收指数
Modified Chlorophyll Absorption Ratio Index		 $\left( \left( \text{RE}-\text{R} \right)-0.2\left( \text{RE}-\text{G} \right) \right)\left( \text{RE}/\text{R} \right)$ [34]
TCARI 转换叶绿素吸收指数
Transformed Chlorophyll Absorption in Reflectance Index		 $3\left( \left( \text{RE}-\text{R} \right)-0.2\left( \text{RE}-\text{G} \right)\left( \text{RE}/\text{R} \right) \right)$ [35]
TCARI/OSAVI 组合植被指数
Combined Spectral Index		 TCARI/OSAVI [36]
VARI 抗大气指数
Visible Atmospherically Resistant Index		 $\left( \text{G}-\text{R} \right)/\left( \text{G}+\text{R}-\text{B} \right)$ [37]
RDVI 重归一化植被指数
Re-normalized Difference Vegetation Index		 $\left( \text{NIR}-\text{R} \right)/\text{sqrt}\left( \text{NIR}+\text{R} \right)$ [38]
MSR 改进比值植被指数
Modified Simple Ratio		 $\left( \text{NIR/R}-1 \right)/\text{sqrt}\left( \text{NIR/R}+1 \right)$ [39]
NGI 归一化绿色指数
Normalized Green Index		 $G/\left( \text{NIR}+\text{G}+\text{RE} \right)$ [40]
NDRE 归一化差值红边指数
Normalized Difference Red Edge Index		 $\left( \text{NIR}-\text{RE} \right)/\left( \text{NIR}+\text{RE} \right)$ [41]

图2

基于不同方法的LAI反演模型构建技术路线图"

表3

玉米LAI数据统计"

生育期
Growth stage		 样本数
Number of samples		 最小值
Min. value		 最大值
Max. value		 平均值
Average value		 标准差
Standard deviation		 方差
Variance		 变异系数
Coefficient of variation (%)
四叶期V4 stage 70 0.37 2.20 0.83 0.34 0.11 40.96
九叶期V9 stage 70 1.61 3.19 2.31 0.34 0.12 14.72

表4

各光谱指数与玉米LAI的相关性分析结果"

光谱指数
Spectral index		 相关系数
Correlation coefficient		 光谱指数
Spectral index		 相关系数
Correlation coefficient
NDVI 0.84** RENDVI 0.81**
RVI 0.85** RESR 0.80**
DVI 0.89** MCARI 0.79**
EVI 0.88** TCARI 0.67**
GNDVI 0.88** TCARI/OSAVI -0.78**
MSAVI 0.88** VARI 0.82**
OSAVI 0.87** RDVI 0.88**
TVI 0.88** MSR 0.86**
GRVI 0.89** NGI -0.88**
SAVI 0.88** NDRE 0.90**

图3

PLSR+ANN模型交叉检验结果"

图4

基于PLSR+ANN的玉米LAI反演模型结果 (a): 建模; (b): 验证。"

图5

PLSR+GPR模型交叉验证结果"

图6

基于PLSR+GPR的玉米LAI反演模结果 (a): 建模; (b): 验证。"

图7

PLSR+SVR模型交叉验证结果"

图8

基于PLSR+SVR的玉米LAI反演模结果 (a): 建模; (b): 验证。"

图9

PLSR+GBDT模型交叉验证结果"

图10

基于PLSR+GBDT的玉米LAI反演模结果 (a): 建模; (b): 验证。"

表5

不同方法下玉米LAI反演结果"

模型
Models		 建模Calibration 验证Validation
Rcal2 RMSEcal Rval2 RMSEval
PLSR+ANN 0.85 0.31 0.89 0.30
PLSR+GPR 0.86 0.30 0.89 0.29
PLSR+SVR 0.86 0.32 0.90 0.33
PLSR+GBDT 0.90 0.25 0.90 0.29
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