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作物学报 ›› 2020, Vol. 46 ›› Issue (5): 787-797.doi: 10.3724/SP.J.1006.2020.91049

• 耕作栽培·生理生化 • 上一篇    下一篇

基于GF-1/2卫星数据的冬小麦叶面积指数反演

吾木提·艾山江1,2,4,买买提·沙吾提1,2,3,*(),陈水森4,李丹4   

  1. 1新疆大学资源与环境科学学院, 新疆乌鲁木齐830046
    2新疆绿洲生态教育部重点实验室, 新疆乌鲁木齐830046
    3新疆智慧城市与环境建模普通高校重点实验室, 新疆乌鲁木齐 830046
    4广州地理研究所, 广东广州 510070
  • 收稿日期:2019-07-28 接受日期:2019-12-26 出版日期:2020-05-12 网络出版日期:2020-01-15
  • 通讯作者: 买买提·沙吾提
  • 作者简介:E-mail: umut710@163.com
  • 基金资助:
    本研究由国家自然科学基金项目资助(41361016)

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 Published:2020-05-12 Published online:2020-01-15
  • Contact: SAWUT Mamat
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(41361016)

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摘要：

叶面积指数(leaf area index, LAI)是监测作物生长状况的重要参数, 准确、快速、大面积估算LAI不仅有助于更好地监测农作物, 而且也有助于其在建模、总体作物管理及精准农业中的应用。本研究为了利用国产遥感影像快速、大面积反演冬小麦LAI, 以GF-1/2影像作为数据源, 提取常用植被指数, 结合不同生育期(起身期、拔节期、开花期)实测LAI数据, 建立反演冬小麦LAI的单变量和多变量经验模型, 并对其进行验证。结果表明, GF-1起身期、GF-1拔节期以及GF-1开花期提取的植被指数中, MSR (modified simple ratio)、GNDVI (green normalized difference vegetation index)、EVI (enhanced vegetation index)与LAI间的相关系数最大, 分别为0.708、0.671和0.743, 说明这些植被指数与冬小麦LAI间的相关性较显著; GF-1不同生育期的反演模型相比, 基于拔节期GNDVIGF-1建立的二次多项式模型和基于开花期EVIGF-1、GSRGF-1 (green simple ratio)、NDVIGF-1 (normalized difference vegetation index)建立的PLSR (partial least squares regression)模型R 2最大, 均为0.783, PLSR模型的RMSE小于二次多项式模型, 说明该多变量模型的稳定性优于单变量模型; 同一个生育期不同影像相比, 基于GF-2的NDVIGF-2建立的二次多项式模型和基于NDVIGF-2、MSRGF-2、SAVIGF-2 (soil-adjusted vegetation index)建立的PLSR模型精度高于基于GF-1的2种模型, R 2分别为0.768和0.809; 不同生育期反演的LAI分布图表明, LAI反演值与实测LAI值基本吻合。以上研究结果说明国产高分辨率遥感影像在农作物生理参数反演中有一定的应用价值, 可以为以后的相关研究提供一定的参考。

关键词: GF-1/2影像, 植被指数, 叶面积指数, 灰色关联度分析, 遥感反演

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 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

图1

研究区示意图"

表1

奇台县冬小麦遥感影像获取时间"

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)

表2

常用宽波段植被指数"

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]
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]

图2

LAI与GF-1/2宽波段植被指数间的相关系数图"

表3

LAI与GF-1/2宽波段植被指数间的灰色关联度及排序"

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) 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)

表4

基于GF-1/2影像宽波段植被指数的LAI单变量估算模型"

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 = 2.9635x2.6672 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起身期
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拔节期
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开花期
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

表5

基于GF-1/2影像宽波段植被指数的LAI多变量估算模型"

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

图3

LAI实测值与预测值拟合分析图"

图4

不同生育期LAI空间分布图"

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