| 光谱植被指数与水稻叶面积指数相关性的研究 |
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| 引用本文： | 薛利红,曹卫星,罗卫红,王绍华.光谱植被指数与水稻叶面积指数相关性的研究[J].植物生态学报,2004,28(1):47-52. |
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| 作者姓名： | 薛利红 曹卫星 罗卫红 王绍华 |
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| 作者单位： | 南京农业大学农业部作物生长调控重点开放实验室,南京,210095 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)，国家自然科学基金 |
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| 摘 要： | 综合分析比较了几种常见光谱植被指数与水稻（Oryza sativa）叶面积指数的相关性及其预测力。结果表明，植被指数的预测力在水稻营养生长旺盛期间最好。植被指数的预测力主要依赖于叶面积指数(LAI)的整体变化范围。因此，综合不同生育时期和氮肥处理的试验资料，光谱植被指数能准确地预测LAI的变化。LAI与各植被指数均呈曲线相关，与比值植被指数(RVI)、再归一化植被指数(RDVI)和R810/R560显著幂相关，与归一化植被指数(NDVI)、垂直植被指数(PVI)、差值植被指数(DVI)、土壤调整植被指数(SAVI)和转换型土壤调整指数(TSAVI)显著指数相关。其中，近红外与绿光波段的比值R810/R560的预测力最佳。用不同移栽秧龄、不同密度、不同水分和氮肥处理的数据对R810/R560的表现进行了检验，结果表明估算精度平均为91.22%，估计的均方差根(RMSE)平均为0.480 5，平均相对误差为-0.013。表明宽波段光谱植被指数可以准确地用来监测水稻叶面积指数。
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| 关 键 词： | 水稻 叶面积指数 植被指数 预测力 光谱监测 |
| 修稿时间： | 2002年11月21 |
RELATIONSHIP BETWEEN SPECTRAL VEGETATION INDICES AND LAI IN RICE |
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| XUE Li-Hong,CAO Wei-Xing,LUO Wei-Hong and WANG Shao-Hua.RELATIONSHIP BETWEEN SPECTRAL VEGETATION INDICES AND LAI IN RICE[J].Acta Phytoecologica Sinica,2004,28(1):47-52. |
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| Authors: | XUE Li-Hong CAO Wei-Xing LUO Wei-Hong and WANG Shao-Hua |
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| Abstract: | Leaf area index (LAI) is an important parameter in crop growth status monitoring and yield forecasting. A vegetation index (VI) based on spectral reflectance measurements has been proposed as a reliable nondestructive method for quickly estimating LAI. To determine the best broadband index for estimating LAI in rice (Oryza sativa), field canopy reflectance values were measured over the whole rice growth cycle using a portable multi-spectral radiometer, and LAI were simultaneously determined by destructive sampling. Several vegetation indices such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil-adjusted vegetation index (SAVI) and the like were derived from these spectral measurements and their correlation with respect to LAI quantified. Also, their relative predictive powers were estimated by comparing determine coefficient (R 2), root mean square error (RMSE) and precision and accuracy. The results showed that the power of VI for LAI assessment was the best during vegetative growth, and mainly depended on the range of variation in the experimental data. Vegetation indices accurately tracked changes in LAI when data were analyzed across a broad range of different growth stages and nitrogen levels. RVI, RDVI, and R 810/R 560 showed a power relation with LAI, while NDVI, PVI, DVI, SAVI and TSAVI showed an exponential relation. R 810/R 560 produced the best estimate of LAI among these indices. The regression equation was tested by independent datasets and the estimation accuracy was about 91.22% with RMSE of 0.480 5 and average relative error of -0.013. The results indicated that LAI monitoring in rice by means of the ratio index of near infrared band to green band from broadband spectral signatures appears very promising. |
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| Keywords: | Rice Leaf area index (LAI) Vegetation indices (VI) Prediction power Reflectance monitoring |
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