Leaf area index estimation of winter wheat based on global sensitivity analysis and machine learning

doi:10.3969/j.issn.1004-1524.2022.09.21

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (9): 2020-2031.DOI: 10.3969/j.issn.1004-1524.2022.09.21

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Leaf area index estimation of winter wheat based on global sensitivity analysis and machine learning

GUO Han¹^,²(), LU Zhou²^,^*(), XU Feifei², LUO Ming², ZHANG Xu¹^,^*()

1. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China
2. Institute of Geographic Sciences and Natural Resources Research, Chines Academy of Sciences, Beijing 100101, China

Received:2021-04-14 Online:2022-09-25 Published:2022-09-30
Contact: LU Zhou,ZHANG Xu

Abstract

Abstract:

In the estimation process of wheat leaf area index (LAI), the method combining spectral variables with machine learning algorithm (MLs) has better performance. However, too many input parameters will lead to data redundancy and reduce the computing efficiency. In order to improve the accuracy of LAI estimation and the efficiency of MLs calculation, a method combining global sensitivity analysis (GSA) and MLs(GSA-MLs) was proposed in this study. Firstly, based on the PROSAIL simulation dataset, GSA was used to quantify the effects of vegetation growth parameters on Sentinel-2 spectral variables. In addition, four variable screening strategies were used to sort all spectral variables, and the optimal variable was selected as the input parameter of MLs. And then, partial least square regression (PLSR), support vector machine (SVM) and random forest (RF), three MLs were used to estimate wheat leaf area index (LAI). The results showed that the red edge vegetation index was mainly affected by chlorophyll content, while the short-wave infrared vegetation index was mainly affected by equivalent water thickness. All spectral variables were subject to interaction between parameters. The 30 spectral variables screened by SLAI-SInteraction performed best in estimating wheat LAI (R²=0.94, RMSE=0.38). Moreover, the running time of model inversion was shortened by 54.13%. This study proposed a combination of global sensitivity analysis and machine learning. In addition to improving the accuracy of LAI estimation by machine learning method and the calculation efficiency in the application process, the machine theory in the application process of machine learning was improved and had good applicability.

Key words: global sensitivity analysis, machine learning, wheat, leaf area index

CLC Number:

S512.1
S330

GUO Han, LU Zhou, XU Feifei, LUO Ming, ZHANG Xu. Leaf area index estimation of winter wheat based on global sensitivity analysis and machine learning[J]. Acta Agriculturae Zhejiangensis, 2022, 34(9): 2020-2031.

Figures/Tables 12

Fig.1 Location of study area and the distribution of ground samples

Table 1 Sentinel-2 band information

波段 Bands	中心波长 Central wavelength/nm	空间分辨率 Space resolution/m
B1-Coastal aerosol	443	60
B2-Blue	490	10
B3-Green	560	10
B4-Red	665	10
B5-red edge (RE-1)	705	20
B6-red edge (RE-2)	740	20
B7-red edge (RE-3)	783	20
B8-NIR	842	10
B8a-Narrow NIR (NNIR)	865	20
B9-water vapour	945	60
B10-SWIR-Cirrus	1 380	60
B11-SWIR-1	1 610	20
B12-SWIR-2	2 190	20

Fig.2 Wheat LAI estimation by using GSA-MLs

Table 2 Parameter settings of PROSAIL model

参数Parameter	范围Range	$X -$ ±s±σ
结构系数Structure coefficient (N)	1.3~1.7	1.5±0.1
叶绿素含量Chlorophyll content (Cab)/(μg·cm^-2)	20~70	45±14
类胡萝卜素含量Carotenoid content (Car)/(μg·cm^-2)	5.0~17.5	—
等效水厚度Equivalent water thickness (Cw)/cm	0.001~0.1	0.050 5±0.028 6
叶片含水量Leaf water content (Cm)/(g·cm^-2)	0.003~0.011	0.007±0.002
褐色素含量Brown pigment content (Cbp)	0.05	—
平均叶倾角Average leaf angle (Lidfa)/(°)	35~70	52.5±10.1
叶面积指数Leaf area index (LAI)	0.1~9.0	4.550 1±2.569 1
热点Hot spot (hspot)	0.05~0.5	0.275±0.130
土壤亮度系数Soil brightness parameter (Psoil)	0~1	0.5±0.3
太阳天顶角Solar zenith angle (tts)/(°)	25	—
观察者天顶角Observer zenith angle (tto)/(°)	30	—
方位Azimuth (psi)/(°)	45	—

Table 2 Parameter settings of PROSAIL model

参数Parameter	范围Range	$X -$ ±s±σ
结构系数Structure coefficient (N)	1.3~1.7	1.5±0.1
叶绿素含量Chlorophyll content (Cab)/(μg·cm^-2)	20~70	45±14
类胡萝卜素含量Carotenoid content (Car)/(μg·cm^-2)	5.0~17.5	—
等效水厚度Equivalent water thickness (Cw)/cm	0.001~0.1	0.050 5±0.028 6
叶片含水量Leaf water content (Cm)/(g·cm^-2)	0.003~0.011	0.007±0.002
褐色素含量Brown pigment content (Cbp)	0.05	—
平均叶倾角Average leaf angle (Lidfa)/(°)	35~70	52.5±10.1
叶面积指数Leaf area index (LAI)	0.1~9.0	4.550 1±2.569 1
热点Hot spot (hspot)	0.05~0.5	0.275±0.130
土壤亮度系数Soil brightness parameter (Psoil)	0~1	0.5±0.3
太阳天顶角Solar zenith angle (tts)/(°)	25	—
观察者天顶角Observer zenith angle (tto)/(°)	30	—
方位Azimuth (psi)/(°)	45	—

Table 3 Vegetation index for estimation of wheat LAI

植被指数 Vegetation index	计算公式 Calculation formula	参考文献 Reference
归一化植被指数Normalized difference vegetation index (NDVI)	(NIR-R))/(NIR+R)	[19]
标准化差异红边指数Normalized difference red-edge index (NDRE)	(NIR-RE)/(NIR+RE)	[20]
比值植被指数Ratio vegetation index (RVI)	NIR/R	[21]
红边比值植被指数Red-edge ratio vegetation index (RERVI)	NIR/RE	[22]
植被指数Difference vegetation index (DVI)	NIR-R	[23]
红边差植被指数Red-edge difference vegetation index (REDVI)	NIR-RE	[19]
绿色植被指数Green difference vegetation index(GDVI)	NIR-G	[19]
红边绿差植被指数Red edge green difference vegetation index (REGDVI)	RE-G	[19]
绿化率植被指数Green ratio vegetation index(GRVI)	NIR/G	[24]
红边绿比植被指数Red edge green ration vegetation index (REGRVI)	RE/G	[1]
绿色归一化植被指数Green normalized difference vegetation index (GNDVI)	(NIR-G)/(NIR+G)	[21]
红边绿归一化差值植被指数	(RE-G)/(RE+G)	[25]
Red edge green normalized difference vegetation index (REGNDVI)
增强型植被指数Enhanced vegetation index (EVI)	2.5×(NIR-R)/(NIR+6·R-7.5·G+1))	[26]
改良增强植被指数Modified enhanced vegetation index (MEVI)	2.5×(NIR-RE)/(NIR+6·RE-7.5·G+1)	[27]
土壤矫正植被指数Soil-adjusted vegetation index (SAVI)	(NIR-R)/(NIR+R+0.25)+0.25	[28]
土壤矫正红边指数Soil-adjusted red-edge index (SARE)	(NIR-RE)/(NIR+R+0.25)+0.25	[29]
绿色叶绿素指数Green chlorophyll index (CIg)	(NIR-G)/G	[30]
叶绿素指数Chlorophyll index (CIre)	(NIR-RE)/RE	[30]
归一化水体指数Normalized difference water index (NDWI)	(NIR-SWIR)/(NIR+SWIR)	[31]
水分胁迫指数Moisture stress index (MSI)	SWIR/NIR	[32]

Table 4 Performance of Sentinel-2 spectral variables in LAI estimation

变量 Variable	精度 R²	均方根误差 RMSE	变量 Variable	精度 R²	均方根误差 RMSE	变量 Variable	精度 R²	均方根误差 RMSE
B	0.251	1.124	GRVI	0.560	1.214	REGNDVI3	0.343	1.130
G	0.177	1.274	GNDVI	0.547	1.172	REGRVI2	0.298	1.191
R	0.373	1.207	SAVI	0.512	1.063	REDVI3	0.286	1.039
RE1	0.083	1.130	RVI	0.511	1.187	REGDVI3	0.279	1.003
RE2	0.136	1.000	MTVI2	0.504	1.079	REGNDVI2	0.271	1.151
RE3	0.255	0.982	EVI	0.496	1.010	SARE3	0.181	1.080
NIR	0.463	0.979	NDVI	0.492	1.132	REGDVI2	0.180	1.023
NNIR	0.286	0.958	GDVI	0.491	1.003	NDRE3	0.155	1.086
SWIR1	0.115	1.333	DVI	0.486	1.002	CIre3	0.153	1.082
SWIR2	0.303	1.548	REDVI1	0.484	1.003	RERVI3	0.153	1.082
SARE2	0.707	0.907	SARE1	0.483	1.067	MEVI2	0.087	1.105
NDRE2	0.681	0.954	MEVI1	0.483	1.018	MEVI3	0.062	1.084
REDVI2	0.678	0.896	NDRE1	0.441	1.131	REGDVI1	0.009	1.049
CIre2	0.667	0.950	CIre1	0.439	1.108	REGRVI1	0.003	1.067
RERVI2	0.667	0.950	RERVI1	0.439	1.108	REGNDVI1	0.003	1.066
Cig	0.560	1.214	REGRVI3	0.365	1.172

Fig.3 Sensitivity of different vegetation growth parameters to different spectral variables

Table 5 Different strategies for sorting spectral variables

序号 No.	策略1 Strategy 1(S_LAI)	策略2 Strategy 2(S_LAI+S_Cab)	策略3 Strategy 3(S_LAI-S_Interaction)	策略4 Strategy 4(S_LAI+S_Cab-S_Interaction)
1	MTVI2	SARE1	MTVI2	MTVI2
2	SAVI	NDRE1	SAVI	SARE1
3	NDVI	MTVI2	NDVI	NDRE1
4	RVI	GNDVI	RVI	SAVI
5	NDRE3	REGNDVI3	DVI	RVI
6	CIRE3	REGNDVI2	REDVI1	SARE2
7	RERVI3	SAVI	REGDVI3	GNDVI
8	DVI	RVI	SARE1	REGNDVI3
9	SARE3	REGRVI2	GDVI	REGNDVI2
10	REDVI1	CIRE1	NDWI2	REGRVI2
11	REGDVI3	RERVI1	SARE3	NDRE2
12	MSI2	REGRVI3	NDRE3	CIRE1
13	SARE1	NDVI	REGDVI2	RERVI1
14	NDWI2	CIg	CIRE3	CIRE2
15	GDVI	GRVI	RERVI3	RERVI2
16	REGDVI2	SARE2	MSI2	REGRVI3
17	REGNDVI2	REGRVI1	EVI	CIg
18	REGNDVI3	REGNDVI1	REGNDVI2	GRVI
19	EVI	NDRE2	REGNDVI3	REDVI1
20	GNDVI	CIRE2	GNDVI	REGRVI1
21	MEVI1	RERVI2	NDRE1	REDVI2
22	R	REDVI2	MEVI1	REGNDVI1
23	NDRE1	REDVI1	RE-3	NDVI
24	RE-3	NDRE3	NIR	REGDVI3
25	NIR	CIRE3	NNIR	DVI
26	NNIR	RERVI3	REDVI2	GDVI
27	MSI1	REGDVI3	MSI1	EVI
28	REGNDVI1	EVI	REGNDVI1	NDWI2
29	REGRVI1	DVI	NDWI1	SARE3
30	REDVI2	GDVI	REGRVI1	REGDVI2

Table 6 Comparison of wheat LAI estimation by GSA-MLs

变量数量 Number of variables	策略Strategy	S_LAI		S_LAI+S_Cab		S_LAI-S_Interaction		S_LAI+S_Cab-S_Interaction
变量数量 Number of variables	模型Modeling	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
10	GSA-PLSR	0.69	0.84	0.69	0.84	0.69	0.84	0.70	0.82
	GSA-SVM	0.40	1.17	0.41	1.16	0.40	1.17	0.41	1.16
	GSA-RF	0.91	0.45	0.90	0.48	0.91	0.46	0.92	0.42
20	GSA-PLSR	0.76	0.73	0.75	0.75	0.76	0.73	0.79	0.69
	GSA-SVM	0.40	1.17	0.44	1.13	0.40	1.17	0.44	1.13
	GSA-RF	0.92	0.44	0.92	0.42	0.92	0.44	0.92	0.42
30	GSA-PLSR	0.87	0.55	0.93	0.40	0.88	0.53	0.92	0.44
	GSA-SVM	0.40	1.17	0.44	1.12	0.40	1.16	0.44	1.12
	GSA-RF	0.93	0.40	0.93	0.40	0.94	0.38	0.94	0.38

Table 7 Comparison of wheat LAI estimation by MLs

机器学习法MLs	R²	RMSE
PLSR	0.81	0.31
SVM	0.41	1.06
RF	0.92	0.40

Table 8 Computer running time in the application process of different estimation models s

建模方法 Modeling methods	机器学习 MLs	全局敏感性机器学习 GSA-MLs
PLSR	487.772	226.270
SVM	495.957	227.190
RF	569.327	261.130

Fig.4 LAI map generated by GSA-RF

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Leaf area index estimation of winter wheat based on global sensitivity analysis and machine learning

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