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黄土高原春小麦叶面积指数与高光谱植被指数相关分析

张凯1;王润元1;王小平1;郭铌1;韩海涛2   

  1. 1中国气象局兰州干旱气象研究所,甘肃省干旱气候变化与减灾重点实验室, 中国气象局干旱气候变化与减灾重点开放实验室, 兰州 730020;2甘肃省气象信息中心, 兰州 730020
  • 收稿日期:2008-03-09 修回日期:1900-01-01 出版日期:2008-10-10 发布日期:2008-10-10

Correlations between leaf area index and hyperspectral vegetation index of spring wheat on Loess Plateau.

ZHANG Kai1;WANG Run-yuan1;WANG Xiao-ping1;GUO Ni1;HAN Hai-tao2   

  1. 1Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, Key and Open Laboratory of Arid Climate Change and Disaster Reduction of CMA, Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China;2Gansu Meteorological Information Center, Lanzhou 730020, China
  • Received:2008-03-09 Revised:1900-01-01 Online:2008-10-10 Published:2008-10-10

摘要: 通过田间小区试验,测定了4个春小麦品种(定西24号、陇春8139、高原602和定西38号)在不同生育期和不同种植密度下的光谱反射率及对应的叶面积指数(LAI)。综合分析比较了9个常见植被指数与春小麦LAI的相关性及预测性。结果表明:在4个不同的生育阶段,这9个植被指数与对应的LAI都有很好的相关性以及对LAI有很好预测性,其中以抽穗开花期植被指数的表现为最好;全生育期这9个植被指数与春小麦LAI的相关性更高,对LAI的预测性更佳,并且大于任何一个生育阶段;其中以近红外与绿光波段的比值R810/R560的预测力最好,故选取R810/R560(x)作为预测全生育期春小麦LAI(y)的最佳植被指数,建立最优模型y=0.1769x1.5261,并采用不同品种和不同种植密度的数据对模型进行了精度分析,结果表明,模拟值和实测值之间的R2平均为0.9280,估算的RMSE平均为0.0762,准确度平均为0.9068,说明此模型具有较好的可靠性和适用性。

关键词: CO2浓度, 长白山, 阔叶红松林, CO2交换

Abstract: In a field plot experiment, the canopy spectral reflectance at different developmental stages of four spring wheat varieties (Dingxi24,Longchun8139, Gaoyuan602 and Dingxi38) under different planting densities was measured, and the leaf area index (LAI) corresponding to the spectra was determined. In order to estimate the LAI and to establish the best prediction model, nine spectral vegetation indices in common use were calculated, and their correlations with and predictabilities for LAI were analyzed and estimated. The results showed that the nine spectral vegetation indices had good correlations with LAI, and could better predict the LAI at four different growth stages, with the best at heading and flowering stages. The correlations between vegetation indices and LAI were more significant and the predictions were better in the whole growth period than in any growth stages. Among the vegetation indices, R810/R560 produced the best prediction of LAI. Therefore, by selecting vegetation index R810/R560(x) as independent variable, the prediction model about spring wheat LAI (y),y=0.1769x1.5261, was established, and evaluated and tested by the experiment data of different varieties and different planting densities. The average precision (R2) and the RMSE and accuracy of the estimation were 0.9280, 0.0762 and 0.9068, respectively, indicating that this prediction model had preferable reliability and wide applicability.

Key words: CO2 concentration, Changbai Mountains, Broad-leaved Korean pine forest, CO2 exchange