Estimation of cotton LAI and SPAD under water-nitrogen coupling based on multi-spectral imaging of unmanned aerial vehicle

doi:10.6048/j.issn.1001-4330.2025.04.001

Abstract

Abstract:

【Objective】 Cotton chlorophyll content and leaf area index are rapidly inferred by UAV using multispectral remote sensing data, which is crucial for predicting yield and making field management decisions. 【Methods】 The cotton in Aral, Xinjiang was taken as the research object, the influencing factors of cotton LAI and SPAD value were taken into consideration in the research, and different irrigation levels and different nitrogen levels were set to create a differentiated canopy structure. Vegetation indexes (VIs) were obtained by using a UAV equipped with multispectral sensors to obtain the canopy images of cotton during the main growth periods, and the mean values (MEA), variance (VAR), synergy (HOM), contrast (CON), dissimilarity (DIS), information (ENT), second-order moment (SEM), correlation (COR) and so on were obtained based on the second-order probabilistic statistical filtering (CO-occurrence measures) method (altogether 8 texture features TFs). The estimation models of cotton LAI and SPAD value based on spectral features, texture features and the combination of the two were established, and the differences were compared. 【Results】 (1) The results showed that the LAI and SPAD value of cotton increased first and then decreased during the whole growth period, and the maximum values of LAI and SPAD value of cotton were at the flowering stage. (2) Four VIs (NDVI, OSAVI, NDCI, RVI) and three TFs (CON, ENT, SEM) with high absolute correlation coefficients were screened out, and cotton LAI and SPAD value estimation models were constructed based on SVR, BPNN, RF, and the highest accuracy of the estimation model was the RF model. (3) The estimation effect of the three input variables on cotton LAI and SPAD value was VIs+TFs, VIs, and TFs in order of accuracy. The fused variables have the highest accuracy for the estimation model of cotton LAI and SPAD value (R²=0.97, RMSE=0.07, R²=0.91, RMSE=1.63). 【Conclusion】 RF algorithm model constructed by using VIs and TFs extracted from multi-spectral remote sensing images of UAV can estimate cotton LAI and SPAD value with high accuracy.

Key words: cotton; leaf area index; chlorophyll content; water-nitrogen coupling; unmanned aerial vehicle; multispectral; texture features

摘要：

【目的】基于多光谱遥感数据通过无人机精度估测棉花叶绿素含量和叶面积指数,为预测产量及精准田间管理提供依据。【方法】以新疆阿拉尔市棉花为研究对象,分析影响棉花叶面积指数(Leaf area index,LAI)和叶绿素相对含量(Chlorophyll relative content,SPAD)的因素,设置不同灌溉水平与不同氮素水平营造差异化的冠层结构。利用搭载多光谱传感器的无人机获取主要生育时期棉花的冠层图像得到植被指数 (Vegetation indexs,VIs),基于二阶概率统计滤波(CO-occurrence measures)方法获取均值(MEA) 方差(VAR)、协同性(HOM)、对比度(CON)、相异性(DIS)、信息(ENT)、二阶矩(SEM)、相关性(COR)等 8 个纹理特征(Texture features,TFs)。分别建立基于光谱特征、纹理特征以及二者结合的棉花LAI与SPAD值的估算模型,并进行差异比较。【结果】(1)棉花LAI和SPAD值在整个生育期呈先上升后下降的趋势,棉花LAI和SPAD值最大值均在花期。(2)筛选出相关系数绝对值高的 4种VIs(NDVI、OSAVI、NDCI、RVI)与3种TFs(CON、ENT、SEM),基于SVR、BPNN、RF构建棉花LAI和SPAD值估测模型,估测模型精度最高为RF模型。(3)3种输入变量对棉花LAI和SPAD值的估测效果按照精度高低排序依次为VIs+TFs、VIs、TFs。融合后的变量对棉花LAI和SPAD值估算模型精度最高(R²=0.97,RMSE=0.07、R²=0.91,RMSE=1.63)。【结论】利用无人机多光谱遥感影像提取VIs与TFs构建的RF算法模型,可以实现对高精度估测棉花LAI和SPAD值。

关键词: 棉花, 叶面积指数, 叶绿素相对含量, 水氮耦合, 无人机, 多光谱, 纹理特征

CLC Number:

S562

ZHAO Yuhang, YAN An, MA Mengqian, XIAO Shuting, SUN Zhe, LI Jingyan. Estimation of cotton LAI and SPAD under water-nitrogen coupling based on multi-spectral imaging of unmanned aerial vehicle[J]. Xinjiang Agricultural Sciences, 2025, 62(4): 781-790.

赵宇航, 颜安, 马梦倩, 肖淑婷, 孙哲, 李靖言. 基于无人机多光谱影像水氮耦合下棉花LAI与SPAD值模型的精度估测[J]. 新疆农业科学, 2025, 62(4): 781-790.

Figures/Tables 9

Fig.1 Location of the study area and the location of the community

Tab.1 Formula for calculating the vegetation index

植被指数 Vegetation Indexes	计算公式 Formula	参考文献 References
归一化差异植被指数Normalized Difference Vegetation Index(NDVI)	(NIR-R)/(NIR+R)	[16]
绿色归一化植被指数Green Normalized Vegetation Index (GNDVI)	(NIR-G)/(NIR+G)	[17]
差值植被指数Difference vegetation index (DVI)	NIR-R	[18]
增强型植被指数Enhanced vegetation index (EVI)	2.5(NIR-R)/(NIR+6R-7.5B+1)	[19]
土壤调节植被指数Soil-regulated vegetation index (SAVI)	(1 + L)(NIR-R)/NIR+R+L	[20]
优化调节植被指数Optimize the adjustment of vegetation index (OSAVI)	1.16(NIR-R)/(NIR+R+0.16)	[21]
修正型土壤调整植被指数Modified soil-adjusted vegetation index (MSAVI)	$(1 + L) (N I R - R E D) N I R + R E D + L$	[22]
比值植被指数Ratio vegetation index (RVI)	NIR/R	[23]
归一化差异红色边缘指数Normalized Difference Red Edge Index (NDRE)	(NIR-RE)/(NIR+RE)	[24]
红绿比值指数 Red green ratio index (RGRI)	R/G	[25]
绿蓝比值指数Blue-green ratio index (BGRI)	B/G	[26]
归一化色素叶绿素比值指数Normalized Pigment Chlorophyll Ratio IndeX(NPCI)	(RE-R)/(RE+R)	[25]
蓝色归一化植被指数Blue Normalized Vegetation Index (BNDVI)	(NIR-B)/(NIR+B)	[27]
再归一化植被指数Renormalize the vegetation index (RDVI)	$N I R - R / N I R + R$	[28]
改进的非线性植被指数Improved nonlinear vegetation index (MNLI)	$(1 + L) (N I R 2 - R) N I R 2 + R + L$	[29]
改进简单植被指数Improved Simple Vegetation Index (MSR)	$N I R R - 1 N I R R + 1$	[30]

Tab.1 Formula for calculating the vegetation index

植被指数 Vegetation Indexes	计算公式 Formula	参考文献 References
归一化差异植被指数Normalized Difference Vegetation Index(NDVI)	(NIR-R)/(NIR+R)	[16]
绿色归一化植被指数Green Normalized Vegetation Index (GNDVI)	(NIR-G)/(NIR+G)	[17]
差值植被指数Difference vegetation index (DVI)	NIR-R	[18]
增强型植被指数Enhanced vegetation index (EVI)	2.5(NIR-R)/(NIR+6R-7.5B+1)	[19]
土壤调节植被指数Soil-regulated vegetation index (SAVI)	(1 + L)(NIR-R)/NIR+R+L	[20]
优化调节植被指数Optimize the adjustment of vegetation index (OSAVI)	1.16(NIR-R)/(NIR+R+0.16)	[21]
修正型土壤调整植被指数Modified soil-adjusted vegetation index (MSAVI)	$(1 + L) (N I R - R E D) N I R + R E D + L$	[22]
比值植被指数Ratio vegetation index (RVI)	NIR/R	[23]
归一化差异红色边缘指数Normalized Difference Red Edge Index (NDRE)	(NIR-RE)/(NIR+RE)	[24]
红绿比值指数 Red green ratio index (RGRI)	R/G	[25]
绿蓝比值指数Blue-green ratio index (BGRI)	B/G	[26]
归一化色素叶绿素比值指数Normalized Pigment Chlorophyll Ratio IndeX(NPCI)	(RE-R)/(RE+R)	[25]
蓝色归一化植被指数Blue Normalized Vegetation Index (BNDVI)	(NIR-B)/(NIR+B)	[27]
再归一化植被指数Renormalize the vegetation index (RDVI)	$N I R - R / N I R + R$	[28]
改进的非线性植被指数Improved nonlinear vegetation index (MNLI)	$(1 + L) (N I R 2 - R) N I R 2 + R + L$	[29]
改进简单植被指数Improved Simple Vegetation Index (MSR)	$N I R R - 1 N I R R + 1$	[30]

Tab.2 Texture features and its formulas

纹理特征 Texture feature	公式 Formula
均值 Mean	$M E A = ∑ i, j N - 1 i P i, j$
方差 Variance	$V A R = ∑ i, j = 0 N - 1 i P i, j (i - M E A) 2$
同质性 Homogeneity	$H O M = ∑ i, j = 0 N - 1 i P i, j 1 + (i - j) 2 ∑ i, j = 0 N - 1 i P i, j 1 + (i - j) 2$
对比度 Contrast	$C O N = ∑ i, j = 0 N - 1 i P i, j (i - j) 2$
差异性 Dissimilarity	$D I S = ∑ i, j = 0 N - 1 i P i, j \| i - j \|$
熵 Entropy	$E N T = ∑ i, j = 0 N - 1 i P i, j (- l n P i, j)$
二阶距 Second Moment	$S E M = ∑ i, j = 0 N - 1 i P i, j 2$
相关性 Correlation	$C O R = ∑ i, j = 0 N - 1 i P i, j [(i - M E A) (j - M E A) V A R i × V A R j]$

Tab.2 Texture features and its formulas

纹理特征 Texture feature	公式 Formula
均值 Mean	$M E A = ∑ i, j N - 1 i P i, j$
方差 Variance	$V A R = ∑ i, j = 0 N - 1 i P i, j (i - M E A) 2$
同质性 Homogeneity	$H O M = ∑ i, j = 0 N - 1 i P i, j 1 + (i - j) 2 ∑ i, j = 0 N - 1 i P i, j 1 + (i - j) 2$
对比度 Contrast	$C O N = ∑ i, j = 0 N - 1 i P i, j (i - j) 2$
差异性 Dissimilarity	$D I S = ∑ i, j = 0 N - 1 i P i, j \| i - j \|$
熵 Entropy	$E N T = ∑ i, j = 0 N - 1 i P i, j (- l n P i, j)$
二阶距 Second Moment	$S E M = ∑ i, j = 0 N - 1 i P i, j 2$
相关性 Correlation	$C O R = ∑ i, j = 0 N - 1 i P i, j [(i - M E A) (j - M E A) V A R i × V A R j]$

Tab.3 Statistics of cotton leaf area index

生育期 Reproductive period	样本量 Sample size	最大值 Maximum	最小值 Minimum	平均值 Mean	标准差 Standard deviation	方差 Variance	变异系数 Variable Coefficient
蕾期Bud stage	45	2.34	1.20	1.65	0.30	0.09	0.18
花期Floresence	45	3.43	1.35	2.31	0.45	0.20	0.19
花铃期Flower and boll stage	45	3.22	1.35	2.25	0.41	0.17	0.18
盛铃期Peak boll stage	45	3.20	1.60	2.12	0.38	0.14	0.16
吐絮期Boll opening stage	45	3.10	1.40	2.10	0.34	0.12	0.16

Tab.4 Statistics of chlorophyll content in cotton

生育期 Reproductive period	样本量 Sample size	最大值 Maximum	最小值 Minimum	平均值 Mean	标准差 Standard deviation	方差 Variance	变异系数 Variable Coefficient
蕾期Bud stage	45	45.57	31.27	39.67	3.55	12.63	0.09
花期Floresence	45	54.94	44.90	51.22	2.32	5.37	0.05
花铃期Flower and boll stage	45	54.55	38.24	46.98	3.93	15.46	0.08
盛铃期Peak boll stage	45	53.48	38.38	45.87	3.22	10.39	0.07
吐絮期Boll opening stage	45	51.62	36.60	45.19	3.99	15.90	0.09

Fig.2 Correlation between vegetation index and texture characteristics and cotton LAI and SPAD value

Tab.5 Evaluation of cotton LAI and SPAD estimation results based on vegetation index

模型 Model	建模集 Modeling sets			验证集 Validation set
模型 Model	R²	RMSE	MSE	R²	RMSE	MSE
SVR_LAI	0.52	0.30	0.09	0.42	0.31	0.1
BPNN_LAI	0.54	0.30	0.09	0.43	0.29	0.1
RF_LAI	0.75	0.11	0.01	0.50	0.31	0.1
SVR_SPAD	0.31	3.28	16.31	0.25	3.71	20.21
BPNN_SPAD	0.42	2.1	14.32	0.35	3.21	14.87
RF_SPAD	0.88	1.86	3.46	0.45	2.27	15.43

Tab.6 Evaluation of cotton LAI and SPAD estimation results based on texture characteristics

模型 Model	建模集 Modeling sets			验证集 Validation set
模型 Model	R²	RMSE	MSE	R²	RMSE	MSE
SVR_LAI	0.51	0.31	0.01	0.44	0.31	0.11
BPNN_LAI	0.61	0.27	0.08	0.50	0.29	0.01
RF_LAI	0.78	0.21	0.01	0.72	0.22	0.01
SVR_SPAD	0.31	4.12	17.90	0.28	4.12	17.32
BPNN_SPAD	0.34	4.41	16.32	0.29	4.21	16.98
RF_SPAD	0.85	1.92	3.94	0.53	2.37	8.47

Tab.7 Evaluation of cotton LAI and SPAD value estimation results based on the combination of vegetation index and texture characteristics

模型 Model	建模集 Modeling sets			验证集 Validation set
模型 Model	R²	RMSE	MSE	R²	RMSE	MSE
SVR_LAI	0.70	0.23	0.01	0.60	0.26	0.01
BPNN_LAI	0.69	0.22	0.05	0.56	0.25	0.06
RF_LAI	0.97	0.07	0.01	0.79	0.20	0.04
SVR_SPAD	0.51	3.32	14.88	0.35	4.01	13.72
BPNN_SPAD	0.47	3.96	15.75	0.45	3.74	14.11
RF_SPAD	0.91	1.63	3.11	0.58	2.67	11.23

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