Hyperspectral Estimation Model for Predicting LAI of Rice in Ningxia Irrigation Zone
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摘要: 水稻叶面积指数（leaf area index，LAI）是评价其长势的重要农学参数，高光谱遥感能够实现叶面积指数的快速无损监测。为了寻找反演水稻LAI的最优植被指数，扩展水稻LAI高光谱估测模型的普适性，选取宁夏引黄灌区水稻为研究对象，通过设置不同氮素处理，借助相关分析、回归分析等方法研究高光谱植被指数与水稻LAI之间的定量关系，并通过确立的最优波段组合，构建4种植被指数与水稻LAI的高光谱反演模型。结果表明，水稻LAI在抽穗末期达到最大值，并随氮素水平的增加而增加；水稻冠层原始光谱反射率在400~722 nm和1 990~2 090 nm波段与LAI达到极显著负相关水平，在近红外区域760~1 315 nm与LAI呈极显著正相关。模型检验结果表明，以比值植被指数RVI（850，750）为变量建立的水稻LAI估测模型最佳，研究结果可为水稻LAI的高光谱估测提供地域参考。Abstract: Leaf area index (LAI) is one of the important parameters for evaluating rice growth status. Hyperspectral remote sensing is a new technical approach that can be used to acquire LAI information quickly and nondestructively. This study aims to explore the best vegetation index and monitoring model for rice LAI inversion. This study was carried out in Ningxia irrigation zone, where the rice was planted in different fertilizer level. Then the correlation between vegetation index and LAI was analyzed and four inversion models were constructed for estimating LAI by using correlation analysis and regression analysis. The result revealed that the LAI value increased with the increase of nitrogen level, and it reached a maximum value at booting stage and then drops down. The reflectance of rice canopy at the wavebands 400~722 nm and 1 990~2 090 nm was very significantly negatively correlated with LAI and that of which at near infrared region (760~1 315 nm) was very significantly positively correlated with LAI. The tests with independent dataset suggested that the rice LAI monitoring models with radio vegetation index RVI (850, 750) as the variable could give an accurate LAI estimation. These results provided an insight for monitoring the rice LAI in different regions.
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Keywords:
- rice /
- leaf area index /
- hyperspectral /
- estimation model /
- Ningxia irrigation region
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图 2 不同氮素水平下不同生育期水稻LAI
Figure 2. LAI of Rice in Differernt Nitrogen Level and Growth Stages
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图 3 不同氮素水平不同生育期冠层光谱曲线特征
Figure 3. Spectral Reflectance of Rice Canopy in Differernt Nitrogen Level and Growth Stages
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图 4 水稻LAI与冠层原始光谱的相关性
Figure 4. Correlation Patterns of LAI to Rice Canopy Spectral Reflectance
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图 5 植被指数估算LAI的决定系数R2分布图
Figure 5. Distribution Plot of the Coefficient of Determination (R2) Between LAI and Vegetation Index
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图 6 高光谱植被指数拟合的水稻LAI预测模型
Figure 6. LAI Estimation Models with Different Hyperspectral Vegetation Indices
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图 7 基于RVI和DVI为参数的水稻LAI预测模型的预测效果
Figure 7. Precision Results of the Estimation Models of LAI Based on RVI and DVI
表 1 估算水稻LAI的高光谱植被指数模型
Table 1 Hyperspectral Vegetation Index Estimation Model of LAI for Rice
变量 模型 回归方程 R2 F 线性 y=22.954x-24.165 0.754 65.28 指数 y=3×10-5e9.565x 0.864 80.56 RVI 对数 y=26.514lnx-1.404 0.740 60.35 二次多项式 y=92.887x2-194.37x +102.35 0.816 73.62 幂 y=0.355x11.137 0.857 74.54 线性 y =11.511x-1.312 0.806 45.76 指数 y=0.415e4.538x 0.827 72.25 DVI 对数 y=3.295 0lnx+6.640 0.676 20.79 二次多项式 y=8.176x2 + 5.2x-0.301 0.812 65.25 幂 y=10.735x1.412 0.820 50.16 线性 y=7.619x-2.299 0.488 21.36 指数 y=0.246e3.188x 0.564 45.26 NDVI 对数 y=4.244lnx+4.766 0.437 15.27 二次多项式 y=4.221x2+ 8.830x-0.746 0.505 20.16 幂 y=4.831x1.831 0.537 38.25 线性 y=8.614x-0.193 8 0.632 37.69 指数 y=0.619e3.503x 0.684 66.75 MSAVI2 对数 y=1.524lnx+4.964 0.424 25.46 二次多项式 y=4.156x2+ 5.511x+0.237 0.616 28.35 幂 y=5.623x0.715 0.638 55.24 
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