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Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (5): 787-797.doi: 10.3724/SP.J.1006.2020.91049


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

HASAN Umut1,2,4,SAWUT Mamat1,2,3,*(),Shui-Sen CHEN4,Dan LI4   

  1. 1College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, Xinjiang, China
    2Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830046, Xinjiang, China
    3Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830046, China
    4Guangzhou Institute of Geography, Guangzhou 510070, Guangdong, China
  • Received:2019-07-28 Accepted:2019-12-26 Online:2020-05-12 Published:2020-01-15
  • Contact: SAWUT Mamat E-mail:korxat@xju.edu.cn
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(41361016)


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 MSRGF-1, NDVIGF-2 (normalized difference vegetation index), GNDVIGF-1 at jointing stage and EVIGF-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 NDVIGF-2 and PLSR model based on NDVIGF-2, MSRGF-2, and SAVIGF-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 GNDVIGF-1 at jointing stage and the PLSR model based on EVIGF-1, GSRGF-1 (green simple ratio) and NDVIGF-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

Fig. 1

Sketch map of the study area"

Table 1

Time of winter wheat remote sensing image acquisition in Qitai county"

Image acquisition time (month/day)
Growth stage of winter wheat
Image name
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)

Table 2

Common wide band vegetation indices"

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]
CIgreen 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]

Fig. 2

Correlation coefficient diagram between LAI and GF-1/2 broad band vegetation indices"

Table 3

Grey correlation degree and order between LAI and GF-1/2 broad band vegetation indices"

起身期 Erecting stage 拔节期 Jointing stage 开花期 Flowering stage
Grey correlation degree (ranking)
Grey correlation degree (ranking)
灰色关联度(排序) Grey correlation degree (ranking) GF-1
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) CIgreen 0.7434(7) EVI2 0.7376(7)
VARI 0.6584(8) CIgreen 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)
CIgreen 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) CIgreen 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)

Table 4

LAI univariate estimation model based on GF-1/2 broad band vegetation indices"

Growth stage
Wide band VI
Model equation
GF-1 Erecting stage
MSR y = 5.4668x-2.6287 0.701 0.494
y = 2.9635x2.6672 0.722 0.477
y = 0.1309e3.1277x 0.716 0.481
y = 6.6008x2-5.0771x+1.4542 0.724 0.475
GF-2 Erecting stage
NDVI y = 12.4782x-2.5336 0.765 0.437
y = 17.8705x2.2060 0.745 0.455
y = 0.2349e5.6859x 0.722 0.477
y = -11.7369x2+21.1498x-4.0829 0.768 0.435
GF-1 Jointing stage
GNDVI y = 8.7911x+0.4153 0.761 0.553
y = 9.1945x0.9409 0.759 0.555
y = 2.2386e1.5225x 0.774 0.537
y = 17.5354x2-13.1548x+7.0072 0.783 0.527
GF-1 Flowering stage
EVI y = 3.1677x+0.6820 0.766 0.379
y = 3.7582x0.9018 0.767 0.378
y = 2.8333e0.4510x 0.766 0.379
y = -0.1721x2+3.8568x+0.0010 0.766 0.379

Table 5

LAI multivariable estimation model based on GF-1/2 broad band vegetation indices"

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

Fig. 3

Fitting analysis chart between measured and predicted values of LAI"

Fig. 4

Spatial distribution map of LAI in different growth stages"

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