基于无人机多光谱的棉花多生育期叶面积指数反演

doi:10.3864/j.issn.0578-1752.2024.01.007

摘要/Abstract

摘要：

【目的】叶面积指数（leaf area index，LAI）是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型，明确不同生育期间棉花LAI估测模型变化规律，为实时掌握棉花长势并因地制宜进行田间科学管理提供依据。【方法】利用大疆精灵4多光谱无人机获取棉花现蕾期、初花期、结铃期、吐絮期多光谱图像和RGB图像。选用归一化差植被指数（NDVI）、绿度归一化差植被指数（GNDVI）、归一化差红边指数（NDRE）、叶片叶绿素指数（LCI）、优化的土壤调节植被指数（OSAVI）5种多光谱指数和修正红绿植被指数（MGRVI）、红绿植被指数（GRVI）、绿叶指数（GLA）、超红指数（EXR）、大气阻抗植被指数（VARI）5种颜色指数分别建立棉花各生育期及棉花生长多生育期数据集合，结合打孔法获取地面LAI实测数据，使用机器学习算法中偏最小二乘（PLSR）、岭回归（RR）、随机森林（RF）、支持向量机（SVM）、神经网络（BP）构建棉花LAI预测模型。【结果】覆膜棉花LAI随着生育期的变化呈现先增长后下降的趋势，现蕾期、初花期、结铃期内侧棉花叶面积指数均值均显著大于外侧（P<0.05）；选择的指数在各时期彼此间均呈显著相关（P<0.05），总体而言，多光谱指数与颜色指数间的相关性随着生育期的进行而呈现下降趋势，选择的指数在各时期均与棉花LAI相关性显著（P<0.05），多光谱指数相关系数介于0.35—0.85，颜色指数相关系数介于0.49—0.71，相关系数绝对值较大的指数多为多光谱指数，颜色指数与棉花LAI的相关系数绝对值较小；估测模型性能结果显示棉花各生育期模型中多光谱指数优于颜色指数，且各指数模型预测性能随着生育期的变化呈现一定规律性，NDVI是预测棉花LAI的最优指数。从模型结果上看，RF模型和BP模型在各生育期下获得了较高的估计精度。初花期LAI反演模型精度最高，最优模型验证集R²为0.809，MAE为0.288，NRMSE为0.120。多生育期最优模型验证集R²为0.386，MAE为0.700，NRMSE为0.198。【结论】棉花内外侧LAI在现蕾期、初花期、结铃期存在显著差异。在各生育期中，RF和BP模型是预测棉花LAI较优模型。NDVI在各指数中表现最好，是预测棉花LAI的最优指数。多生育期模型效果较单生育期明显下降，最优指数为GNDVI，最优模型为BP。本研究中预测棉花LAI的最优窗口期是初花期。研究结果可为无人机遥感监测棉花LAI提供理论依据和技术支持。

关键词: 棉花, 叶面积指数, 多光谱指数, 颜色指数, 无人机多光谱, 机器学习

Abstract:

【Objective】The leaf area index (LAI) is a vital indicator for evaluating crop growth, photosynthesis, and transpiration. The objective of this study is to explore the cotton LAI estimation models based on multi-spectral data from drones at different growth stages and multiple growth stages, clarify the variation patterns of cotton LAI estimation models during different growth stages, and to provide a basis for real-time understanding of cotton growth and scientific field management tailored to local conditions. 【Method】The DJI Elf 4 multi-spectral UAV was used to acquire multi-spectral images and RGB images of cotton at budding stage, initial flowering stage, boll setting and open-boll stages. Five multi-spectral indices, namely normalized difference vegetation index (NDVI), normalized green difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), leaf chlorophyll index (LCI), optimized soil adjusted vegetation index (OSAVI), and five color indices, namely modified green-red vegetation index (MGRVI), green-red vegetation index (GRVI), green leaf algorithm (GLA), excess red index (EXR), and visible atmospherically resistant vegetation index (VARI), were selected to build a data set for each growth stage of cotton and multiple growth stages of cotton growth, respectively. Combined with the punching method to obtain actual ground LAI data, the machine learning algorithms of partial least squares regression (PLSR), ridge regression (RR), random forest (RF), support vector machine (SVM) and back propagation (BP) were used to construct a cotton LAI prediction model. 【Result】The LAI of cotton exhibited an increasing and then decreasing pattern during the growth stage. Notably, the mean LAI values of cotton at the inner side of the budding stage, initial flowering stage, and boll setting stage were significantly greater than those at the lateral side (P<0.05). The selected indices exhibited significant correlations with each other across the periods (P<0.05). In general, the correlation between multi-spectral index and color index showed a decreasing trend as the growth stage progressed, and the selected indices were significantly correlated with cotton LAI in all stages (P<0.05), the correlation coefficients of multi-spectral index ranged from 0.35 to 0.85, and the correlation coefficients of color index ranged from 0.49 to 0.71, and those with a larger absolute value of the correlation coefficients were mostly multi-spectral indices, while those of the correlation coefficients of color index and cotton LAI were smaller. The estimated model performance results showed that the multi-spectral index was better than the color index in the cotton growth models, the predictive performance of the index models showed certain regularity with the change of growth, and NDVI was the optimal index for predicting cotton LAI. From the model results, the RF model and BP model obtained higher estimation accuracy under each growth stage. The LAI inversion model at the initial flowering stage had the highest accuracy, with the optimal model validation set R² of 0.809, MAE of 0.288, and NRMSE of 0.120. The optimal model validation set for the multiple growth stages had the R² of 0.386, MAE of 0.700, and NRMSE of 0.198. 【Conclusion】There are significant differences in LAI between the inner and lateral sides of cotton during the budding stage, initial flowering stage, and boll setting stage. NDVI emerged as the optimal index for predicting cotton LAI at all growth stages, with the RF and BP models demonstrating superior performance. The effectiveness of the multiple growth stages model was notably lower compared to that of the single-growth model, with the optimal index identified as GNDVI and the optimal model as BP. The initial flowering stage appeared to be the optimal window for predicting cotton LAI. These findings can provide theoretical basis and technical support for utilizing UAV remote sensing to monitor cotton LAI.

Key words: cotton, leaf area index (LAI), multi-spectral index, color index, drone multi-spectral, machine learning

石浩磊, 曹红霞, 张伟杰, 朱珊, 何子建, 张泽. 基于无人机多光谱的棉花多生育期叶面积指数反演[J]. 中国农业科学, 2024, 57(1): 80-95.

SHI HaoLei, CAO HongXia, ZHANG WeiJie, ZHU Shan, HE ZiJian, ZHANG Ze. Leaf Area Index Inversion of Cotton Based on Drone Multi-Spectral and Multiple Growth Stages[J]. Scientia Agricultura Sinica, 2024, 57(1): 80-95.

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试验1 Test 1	灌水量Irrigation amount		异次地表地下交替滴灌开始时间 Start time of alternative surface/subsurface drip irrigation
试验1 Test 1	苗期、现蕾期、初花期的第1次灌水（淋洗水量+灌溉水量） The first irrigation at seedling stage, budding stage and initial flowering stage (Leaching water+Irrigation water)	其他生育阶段 Other growth stages
W1Z1	40 mm+80% ET_C	80% ET_C	苗期Seedling stage
W1Z2	40 mm+80% ET_C	80% ET_C	现蕾期Budding stage
W1Z3	40 mm+80% ET_C	80% ET_C	初花期Initial flowering stage
W2Z1	80 mm+80% ET_C	80% ET_C	苗期Seedling stage
W2Z2	80 mm+80% ET_C	80% ET_C	现蕾期Budding stage
W2Z3	80 mm+80% ET_C	80% ET_C	初花期Initial flowering stage
W3Z1	120 mm+80% ET_C	80% ET_C	苗期Seedling stage
W3Z2	120 mm+80% ET_C	80% ET_C	现蕾期Budding stage
W3Z3	120 mm+80% ET_C	80% ET_C	初花期Initial flowering stage

试验2 Test 2	灌水量Irrigation amount		地表、地下滴灌水量分配（地表滴灌+地下滴灌） Surface and subsurface drip irrigation water distribution (Surface drip irrigation+Subsurface drip irrigation)
试验2 Test 2	苗期、现蕾期、初花期的第1次灌水（淋洗水量+灌溉水量） The first irrigation at seedling stage, budding stage and initial flowering stage (Leaching water+Irrigation water)	其他生育阶段 Other growth stages
W1F1	40 mm+80% ET_C	80% ET_C	100%+0
W1F2	40 mm+80% ET_C	80% ET_C	75%+25%
W1F3	40 mm+80% ET_C	80% ET_C	50% +50%
W1F4	40 mm+80% ET_C	80% ET_C	25%+75%
W1F5	40 mm+80% ET_C	80% ET_C	0+100%
W2F1	80 mm+80% ET_C	80% ET_C	100%+0
W2F2	80 mm+80% ET_C	80% ET_C	75%+25%
W2F3	80 mm+80% ET_C	80% ET_C	50%+50%
W2F4	80 mm+80% ET_C	80% ET_C	25%+75%
W2F5	80 mm+80% ET_C	80% ET_C	0+100%
W3F1	120 mm+80% ET_C	80% ET_C	100%+0
W3F2	120 mm+80% ET_C	80% ET_C	75%+25%
W3F3	120 mm+80% ET_C	80% ET_C	50%+50%
W3F4	120 mm+80% ET_C	80% ET_C	25%+75%
W3F5	120 mm+80% ET_C	80% ET_C	0+100%

棉花生育期划分 Classification of cotton growth stage	无人机数据采集时间 UAV data collection time	棉花生长指标数据采集时间 Cotton growth indicator data collection time
现蕾期Budding stage	2021-06-17	2021-06-17
初花期Initial flowering stage	2021-07-19	2021-07-19
结铃期Boll setting stage	2021-08-12	2021-08-12
吐絮期Open-boll stage	2021-09-07	2021-09-07

相关指数 Correlation index		计算公式 Formula	参考文献 Reference
多光谱指数 Multi-spectral index	归一化差植被指数 Normalized difference vegetation index (NDVI)	$ \mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\mathrm{Red}} $	[29]
	绿度归一化差植被指数 Normalized green difference vegetation index (GNDVI)	$\text{GNDVI}=\frac{\text{NIR-Green}}{\text{NIR}+\text{Green}}$	[30]
	归一化差红边指数 Normalized difference red-edge vegetation index (NDRE)	$ \text { NDRE }=\frac{\text { NIR-RedEdge }}{\text { NIR }+ \text { RedEdge }} $	[31]
	叶片叶绿素指数 Leaf chlorophyll index (LCI)	$ \mathrm{LCI}=\frac{\text { NIR-RedEdge }}{\text { NIR }+ \text { Red }} $	[32]
	优化的土壤调节植被指数 Optimized soil adjusted vegetation index (OSAVI)	$ \text { OSAVI }=\frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\operatorname{Red}+0.16} $	[33]
颜色指数 Color index	修正红绿植被指数 Modified green-red vegetation index (MGRVI)	$ \text { MGRVI }=\frac{g^{2}-r^{2}}{g^{2}+r^{2}} $	[34]
	红绿植被指数 Green-red vegetation index (GRVI)	$ \text { GRVI }=\frac{g-r}{g+r} $	[34]
	绿叶指数 Green leaf algorithm (GLA)	$\text{GLA}=\frac{\text{2g-r}+\text{b}}{\text{2g}+\text{r}+\text{b}}$	[35]
	超红指数 Excess red index (EXR)	EXR=1.4r-g	[36]
	大气阻抗植被指数 Visible atmospherically resistant vegetation index (VARI)	$ \mathrm{VARI}=\frac{\mathrm{g}-\mathrm{r}}{\mathrm{g}+\mathrm{r}-\mathrm{b}} $	[37]

生育期 Growth stage	采样点 Sampling point	样本数 Number of samples	均值 Mean value	方差 Variance	变异系数 Variability coefficient (%)	显著性分析 Significance analysis
现蕾期 Budding stage	内侧Inner	18	2.71	0.47	17.51	P<0.05
现蕾期 Budding stage	外侧Lateral	18	2.20	0.57	25.73	P<0.05
初花期 Initial flowering stage	内侧Inner	24	4.79	0.94	19.72	P<0.05
初花期 Initial flowering stage	外侧Lateral	24	3.79	0.80	21.09	P<0.05
结铃期 Boll setting stage	内侧Inner	18	4.27	1.02	23.80	P<0.05
结铃期 Boll setting stage	外侧Lateral	18	3.51	0.81	23.19	P<0.05
吐絮期 Open-boll stage	内侧Inner	24	3.71	0.80	21.45	0.05<P<0.1
吐絮期 Open-boll stage	外侧Lateral	24	3.29	0.75	22.87	0.05<P<0.1