基于GF-1/2卫星数据的冬小麦叶面积指数反演

doi:10.3724/SP.J.1006.2020.91049

Abstract

Abstract:

Leaf area index (LAI) is an important parameter for monitoring crop growth, and an important input parameter for crop yield prediction model, hydrological and climatic models. LAI can be used in field crop growth monitoring and verification of remote sensing products. Therefore, accurate, rapid and large-scale estimation of LAI is not only conducive to better monitoring crops, but also conducive to its application in modeling, crop management and precision agriculture. Remote sensing technique has become a promising method to detect and monitor the crop LAI due to its many advantages. In this paper, Banjiequan Village, Wumachang Township, Qitai County, Xinjiang, China was selected as the study area. In order to rapidly and extensively retrieve LAI of winter wheat using domestic remote sensing images, 17 common vegetation indices were extracted from the GF-1/2 images, which was synchronized with field sampling, and observation at intervals of 20 m in the east-west direction and 30 m in the north-south direction in a 130 m × 420 m block. A total of 78 sampling points were taken from a small area of 4 rows × 50 cm. Sampling width was measured by tape ruler and coordinates of sampling points were given by GPS. Based on the vegetation indices extracted from GF-1/2 image and LAI data measured at erecting stage, jointing stage and flowering stage, we established univariate (linear, exponential, power, quadratic polynomials) and multivariate (partial least squares regression, PLSR) empirical models for inversion of winter wheat LAI, and validated them. The correlation coefficients of LAI with MSR (modified simple ratio), GNDVI (green normalized difference vegetation index), EVI (enhanced vegetation index) extracted from the erecting, jointing and flowering stages of GF-1 were the maximum, which were 0.708, 0.671, and 0.743, respectively, indicating that the correlation between these vegetation indices and LAI of winter wheat was significant. The univariate model R ² based on MSR_GF-1, NDVI_GF-2 (normalized difference vegetation index), GNDVI_GF-1 at jointing stage and EVI_GF-1 at flowering stage were all greater than 0.7. Compared with different image data at the same growth stage, the quadratic polynomial model based on NDVI_GF-2 and PLSR model based on NDVI_GF-2, MSR_GF-2, and SAVI_GF-2 (soil-adjusted vegetation index) were more precise than those based on GF-1, with R ² of 0.768 and 0.809 respectively. Compared with the models with the same data (GF-1) at different growth stages, the quadratic polynomial model based on GNDVI_GF-1 at jointing stage and the PLSR model based on EVI_GF-1, GSR_GF-1 (green simple ratio) and NDVI_GF-1 at flowering stage had the maximum value of R ², which was 0.783. The RMSE of the PLSR model was smaller than that of the quadratic polynomial model, indicating the stability of the multivariate model was better than univariate model. Analyzing the LAI distribution maps inverted from different growth stages, it was found that the LAI inversion values basically coincided with the measured LAI values. The above results show that the domestic high-resolution remote sensing image has certain application value in crop physiological parameter inversion, and provides some references for related researches in the future.

Key words: GF-1/2 image, vegetation index, leaf area index, grey correlation analysis, remote sensing inversion

HASAN Umut,SAWUT Mamat,Shui-Sen CHEN,Dan LI. Inversion of leaf area index of winter wheat based on GF-1/2 image[J].Acta Agronomica Sinica, 2020, 46(5): 787-797.

Figures/Tables 9

Fig. 1

Table 1

Table 2

Common wide band vegetation indices"

植被指数 VI	名称 Name	公式 Equation	参考文献 Reference
NDVI	归一化差值植被指数 Normalized Difference Vegetation Index	$(R_{NIR}-R_{red})/(R_{NIR}+R_{red})$	[18]
GNDVI	绿度归一化差值植被指数 Green Normalized Difference Vegetation Index	$(R_{NIR}-R_{green})/(R_{NIR}+R_{green})$	[19]
RDVI	复归一化差值植被指数 Renormalized Difference Vegetation Index	$(R_{NIR}-R_{red})/ \sqrt{R_{NIR}+R_{red}}$	[20]
SAVI	土壤调整植被指数 Soil-Adjusted Vegetation Index	$(1+0.1)/ \frac{R_{NIR}-R_{red}}{R_{NIR}+R_{red}+0.1}$	[21]
MSR	修正简单比 Modified Simple Ratio	$(R_{NIR}-R_{blue})/(R_{red}+R_{blue})$	[22]
TVI	三角植被指数 Triangular Vegetation Index	$0.5[120(R_{NIR}-R_{green})-200(R_{red}+R_{green})]$	[18]
OSAVI	优化土壤调整植被指数 Optimized Soil-Adjusted Vegetation Index	$1.16(R_{NIR}-R_{red})/(R_{NIR}+R_{red}+0.16)$	[23]
EVI	增强植被指数 Enhanced Vegetation Index	$2.5 \times \frac{R_{NIR}-R_{red}}{R_{NIR}+6 \times R_{red}-7.5 \times R_{blue}+1}$	[24]
EVI2	双波段增强植被指数 Two-band Enhanced Vegetation Index	$2.5(R_{NIR}-R_{red})/(R_{NIR}+2.4 \times R_{red}+1)$	[25]
GSR	绿度简单比 Green Simple Ratio	${R_{NIR}}/{R_{green}}$	[26]
DVI	差值植被指数 Difference Vegetation Index	$R_{NIR}-R_{red}$	[27]
WDRVI	宽动态植被指数 Wide Dynamic Range Vegetation Index	$0.1\times(R_{NIR}-R_{red})/0.1 \times(R_{NIR}+R_{red})$	[28]
CI_green	绿色叶绿素指数 Green Chlorophyll Index	$\frac{R_{NIR}}{R_{green}}-1$	[29]
VARI	可见光大气阻力指数 Visible Atmospherically Resistant Index	$(R_{green}-R_{red})/(R_{green}+R_{red}-R_{blue})$	[30]
MTVI	修正三角植被指数 Modified Triangular vegetation index	$1.2[1.2(R_{NIR}-R_{green})-200(R_{red}-R_{green})]$	[18]
NLI	非线性植被指数 Nonlinear Vegetation Index	$(R^{2}_{NIR}-R_{red})/(R^{2}_{NIR}+R_{red})$	[31]
MNLI	修正非线性植被指数 Modified Nonlinear Vegetation Index	$1.5(R^{2}_{NIR}-R_{red})/(R^{2}_{NIR}+R_{red}+0.5)$	[31]

Table 2

Fig. 2

Table 3

Table 4

Table 5

Fig. 3

Fig. 4

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影像获取时间 Image acquisition time (month/day)	冬小麦生育期 Growth stage of winter wheat	影像名称 Image name	波段信息 Band information
4/14, 4/16	起身期 Erecting stage	GF-1, GF-2	GF-1/2: 多光谱 Multispectral (0.45-0.52, 0.52-0.59, 0.63-0.69, 0.77-0.89 μm)
5/9	拔节期 Jointing stage	GF-1
6/6	开花期 Flowering stage	GF-1	GF-2: 全色 Panchromatic (0.45-0.90 μm)

起身期 Erecting stage				拔节期 Jointing stage		开花期 Flowering stage
GF-1 VI	灰色关联度(排序) Grey correlation degree (ranking)	GF-2 VI	灰色关联度(排序) Grey correlation degree (ranking)	GF-1 VI	灰色关联度(排序) Grey correlation degree (ranking)	GF-1 VI	灰色关联度(排序) Grey correlation degree (ranking)
MSR	0.8015(1)	NDVI	0.8657(1)	GNDVI	0.8154(1)	EVI	0.7894(1)
EVI2	0.7607(2)	MSR	0.8622(2)	NDVI	0.8006(2)	GSR	0.7737(2)
OSAVI	0.7570(3)	SAVI	0.8582(3)	SAVI	0.7890(3)	NDVI	0.7450(3)
GSR	0.7211(4)	DVI	0.8545(4)	GSR	0.7693(4)	SAVI	0.7417(4)
SAVI	0.6839(5)	MTVI	0.8523(5)	OSAVI	0.7448(5)	OSAVI	0.7417(5)
NDVI	0.6687(6)	RDVI	0.8508(6)	EVI2	0.7440(6)	GNDVI	0.7413(6)
TVI	0.6590(7)	VARI	0.8500(7)	CI_green	0.7434(7)	EVI2	0.7376(7)
VARI	0.6584(8)	CI_green	0.8495(8)	WDRVI	0.7424(8)	WDRVI	0.7325(8)
MTVI	0.6366(9)	MSR	0.8494(9)	RDVI	0.7406(9)	MSR	0.7235(9)
CI_green	0.6360(10)	WDRVI	0.8488(10)	EVI	0.7399(10)	VARI	0.7195(10)
WDRVI	0.6340(11)	EVI2	0.8485(11)	MSR	0.7366(11)	RDVI	0.7176(11)
DVI	0.6327(12)	OSAVI	0.8483(12)	MTVI	0.7318(12)	MTVI	0.7175(12)
EVI	0.6306(13)	EVI	0.8482(13)	DVI	0.7312(13)	CI_green	0.7173(13)
GNDVI	0.6293(14)	MNLI	0.8470(14)	VARI	0.7308(14)	DVI	0.7155(14)
RDVI	0.6225(15)	NLI	0.8305(15)	MNLI	0.7172(15)	NLI	0.7127(15)
NLI	0.6098(16)	GSR	0.8303(16)	NLI	0.7169(16)	MNLI	0.7126(16)
MNLI	0.6096(17)	TVI	0.8167(17)	TVI	0.6898(17)	TVI	0.6530(17)

生育期 Growth stage	宽波段VI Wide band VI	模型方程 Model equation	R²	RMSE
GF-1起身期 GF-1 Erecting stage	MSR	y = 5.4668x-2.6287	0.701	0.494
		y = 2.9635x^2.6672	0.722	0.477
		y = 0.1309e^3.1277^x	0.716	0.481
		y = 6.6008x²-5.0771x+1.4542	0.724	0.475
GF-2起身期 GF-2 Erecting stage	NDVI	y = 12.4782x-2.5336	0.765	0.437
		y = 17.8705x^2.2060	0.745	0.455
		y = 0.2349e^5.6859^x	0.722	0.477
		y = -11.7369x²+21.1498x-4.0829	0.768	0.435
GF-1拔节期 GF-1 Jointing stage	GNDVI	y = 8.7911x+0.4153	0.761	0.553
		y = 9.1945x^0.9409	0.759	0.555
		y = 2.2386e^1.5225^x	0.774	0.537
		y = 17.5354x²-13.1548x+7.0072	0.783	0.527
GF-1开花期 GF-1 Flowering stage	EVI	y = 3.1677x+0.6820	0.766	0.379
		y = 3.7582x^0.9018	0.767	0.378
		y = 2.8333e^0.4510^x	0.766	0.379
		y = -0.1721x²+3.8568x+0.0010	0.766	0.379

生育期 Growth stage	模型方程 Model equation	R²	RMSE
GF-1起身期 GF-1 Erecting stage	LAI=2.0666×MSR+2.4609×EVI2+5.8022×OSAVI-4.2774	0.751	0.451
GF-2起身期 GF-2 Erecting stage	LAI=6.8168×NDVI+3.1975×MSR+1.5024×SAVI-3.0187	0.809	0.395
GF-1拔节期 GF-1 Jointing stage	LAI=2.4087×GNDVI+2.0794×NDVI+1.6322×SAVI+0.1967×GSR +0.7757	0.760	0.554
GF-1开花期 GF-1 Flowering stage	LAI=1.2643×EVI+0.2265×GSR+4.2343×NDVI+0.0108	0.783	0.365

Inversion of leaf area index of winter wheat based on GF-1/2 image

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[7]	WU Qiong,QI Bo,ZHAO Tuan-Jie,YAO Xin-Feng,ZHU Yan,GAI Jun-Yi. A Tentative Study on Utilization of Canopy Hyperspectral Reflectance to Esti-mate Canopy Growth and Seed Yield in Soybean [J]. Acta Agron Sin, 2013, 39(02): 309-318.
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