基于无人机高光谱和集成学习的春小麦叶绿素含量反演

doi:10.13304/j.nykjdb.2024.0010

摘要/Abstract

摘要：

叶绿素含量是监测作物长势的关键指标，快速、有效、准确的估算对作物健康评估具有重要意义。通过采集3个生长期的无人机高光谱影像，结合地面叶绿素实测数据，选用多种机器学习和集成学习模型，反演春小麦叶绿素含量，并对比不同模型的反演精度。结果表明，春小麦不同生长期冠层反射率基本一致，但在770~900 nm 波长范围内显示出明显的光谱反射率强度差异。16种光谱指数均与叶绿素含量呈显著相关，其中优化植被指数1、植物生化指数和归一化差异红边指数在整个生长周期内均与叶绿素含量保持高相关性。Stacking和Voting集成学习模型的预测精度高于基础模型，其中Voting集成学习模型表现更突出，测试集中3个生长期的决定系数（R²）分别为0.78、0.77和0.73，均方根误差（root mean square error，RMSE）分别为8.70、11.36和16.17；与随机森林、支持向量机、K_近邻和岭回归相比，其R²平均分别提高约0.17、0.14和0.22，RMSE平均降低4.64、2.54和6.51，显示出良好的预测能力。研究结果可为精准农业和作物健康监测提供新的视角和方法。

关键词: 无人机, 高光谱遥感, 春小麦, 叶绿素含量, 集成学习

Abstract:

Chlorophyll content is a key indicator for monitoring crop growth， and its rapid， effective and accurate estimation is crucial for assessing crop health. By collecting unmanned aerial vehicle （UAV） hyperspectral images from 3 growth stages and combining them with ground-measured chlorophyll data， various machine learning and ensemble learning models were employed to estimate the chlorophyll content in spring wheat，and the estimation accuracy of different models were compared. The results showed that the canopy reflectance of spring wheat was generally consistent across different growth stages， but significant differences in spectral reflectance intensity were observed in the 770~900 nm wavelength range. 16 spectral indices all showed significant correlations with chlorophyll content， among which optimized vegetation index 1， plant biochemical index and normalized difference red edge index maintained high correlation throughout the entire growth cycle. The prediction accuracy of the Stacking and Voting ensemble learning models was higher than the basic models， with the Voting ensemble model performing particularly well. In the test set， determination coefficient （R²）values of 3 growth stages were 0.78， 0.77 and 0.73， and root mean square error （RMSE） values were 8.70， 11.36 and 16.17， respectively. Compared with random forest， support vector regression， K_nearest neighbor and ridge regression models， the R² of the Voting model was on average 0.17， 0.14 and 0.22 higher， and the RMSE was 4.64， 2.54 and 6.51 lower， indicating its superior predictive ability. Above results provided new perspectives and methods for precision agriculture and crop health monitoring.

Key words: unmanned aerial vehicle（UAV）, hyperspectral remotesensing, spring wheat, chlorophyll content, ensemble learning

中图分类号:

S127

呼斯乐, 包玉龙, 图布新巴雅尔null, 陶际峰, 郭恩亮. 基于无人机高光谱和集成学习的春小麦叶绿素含量反演[J]. 中国农业科技导报, 2025, 27(6): 93-103.

Sile HU, Yulong BAO, Tubuxinbayaer, Jifeng TAO, Enliang GUO. Chlorophyll Content Inversion of Spring Wheat Based on Unmanned Aerial Vehicle Hyperspectral and Integrated Learning[J]. Journal of Agricultural Science and Technology, 2025, 27(6): 93-103.

图/表 10

图1 高光谱数据拼接流程

Fig. 1 Hyperspectral data splicing process

图2 LCC反演集成学习流程

Fig. 2 Integrated learning flow for LCC estimation

表1 不同生长期春小麦LCC变化特征

Table 1 Characteristics of changes in LCC in spring wheat at different growth stages

生长期

Growth stage

平均值

Mean value

最大值

Maximum value

最小值

Minimum value

标准差

Standard deviation

变异系数

Coefficient of variation/%

图3 春小麦不同生长期LCC变化箱线图

Fig. 3 Box plot of LCC changes in spring wheat at different growth stages

图4 春小麦不同生长期光谱反射率

Fig. 4 Spectral reflectance of spring wheat at different growth stages

图5 光谱指数与LCC相关性热力图注：B—蓝光波段；G—绿光波段；R—红光波段；NIR—近红外波段；RVI—比值植被指数；NDVI—归一化差异植被指数；EVI—增强型植被指数；EVI 2—双波段增强型植被指数；OVG1—优化植被指数1；NDRE—归一化差异红边指数；PBI—植物生化指数；NDVI705—705 nm归一化差异植被指数；mSR705—705 nm比值植被指数；PRI—光化学反射指数；SIPI—结构不敏感色素指数；PSRI—植物衰老反射指数；LCC—叶绿素含量。*和**分别表示在P<0.05和P<0.01水平显著相关。

Fig. 5 Heatmap of the correlation between spectral indices and LCCNote： B—Blue；G—Green；R—Red；NIR—Near infrared；RVI—Ratio vegetation index；NDVI—Normalized difference vegetation index；EVI—Enhanced vegetation index；EVI 2—Two-band enhanced vegetation index；OVG1—Optimized vegetation index 1；NDRE—Normalized difference red edge index；PBI—Plant biochemical index；NDVI705—Normalized difference vegetation index705；mSR705—Modified simple ratio705；PRI—Photochemical reflectance index；SIPI—Structure insensitive pigment index；PSRI—Plant senescence reflectance Index；LCC—Leaf chlorophyll content. * and ** indicate significant correlations at P<0.05 and P<0.01 levels， respectively.

表2 春小麦不同生长期LCC反演模型训练集

Table 2 Training set for LCCinversion model of spring wheat at different growth stages

生长期 Growth stage	评价指标 Evaluation index	随机森林 RF	支持向量机 SVR	K_近邻 K_NN	岭回归 RR	Stacking	Voting
拔节期 Jointing stage	决定系数R²	0.73	0.66	0.66	0.51	0.77	0.80
拔节期 Jointing stage	均方根误差RMSE	11.94	14.07	14.61	15.78	12.21	11.50
抽穗期 Heading stage	决定系数R²	0.71	0.64	0.61	0.57	0.72	0.80
抽穗期 Heading stage	均方根误差RMSE	12.52	14.50	17.29	15.11	12.25	10.77
灌浆期 Filling stage	决定系数R²	0.71	0.55	0.44	0.43	0.71	0.75
灌浆期 Filling stage	均方根误差RMSE	16.60	22.69	27.92	22.64	17.10	15.52

表3 春小麦不同生长期LCC反演模型测试集

Table 3 Test set for LCC inversion model of spring wheat at different growth stages

生长期 Growth stage	评价指标 Evaluation index	随机森林 RF	支持向量机 SVR	K_近邻 K_NN	岭回归 RR	Stacking	Voting
拔节期 Jointing stage	决定系数R²	0.72	0.64	0.60	0.49	0.75	0.78
拔节期 Jointing stage	均方根误差RMSE	13.73	10.47	13.10	16.06	9.39	8.70
抽穗期 Heading stage	决定系数R²	0.68	0.61	0.64	0.59	0.70	0.77
抽穗期 Heading stage	均方根误差RMSE	13.24	13.19	14.00	15.20	13.13	11.36
灌浆期 Filling stage	决定系数R²	0.67	0.50	0.42	0.44	0.68	0.73
灌浆期 Filling stage	均方根误差RMSE	17.83	23.67	26.17	23.09	19.98	16.18

图6 基于Voting集成学习模型的不同生长期春小麦LCC反演散点图

Fig. 6 Scatter plots of LCC inversion in spring wheat at different growth stages based on the Voting ensemble learning model

图7 春小麦不同生长期LCC变化反演图

Fig. 7 Inversion graphs of LCC changes in spring wheat at different growth stages

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