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用于估算水稻叶面积指数的最佳波段识别

Optimal waveband identification for estimation of leaf area index of paddy rice.

作者信息

Wang Fu-min, Huang Jing-feng, Zhou Qi-fa, Wang Xiu-zhen

机构信息

Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China.

出版信息

J Zhejiang Univ Sci B. 2008 Dec;9(12):953-63. doi: 10.1631/jzus.B0820211.

Abstract

The objectives of the study were to select suitable wavebands for rice leaf area index (LAI) estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance (350 approximately 2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation: correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350~2 500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1 633 nm. They were followed by 444, 524, 576, 594, 804, 849, 974, 1 074, 1 219, 1 510, and 2 194 nm. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.

摘要

本研究的目的是利用整个生长季获取的数据选择适合估算水稻叶面积指数(LAI)的波段,并通过与水稻冠层光谱特征位置进行比较来检验所选波段的有效性。本研究于2002年生长季在中国杭州浙江大学实验农场进行田间试验。在整个生长季对水稻冠层进行了高光谱反射率(350~2500nm)测量及相应叶面积指数测量。采用了三种方法来确定用于估算水稻叶面积指数的最佳波段:基于相关系数的方法、基于植被指数的方法和逐步回归法。本研究在350~2500nm区域选择了15个波段,这些波段似乎是估算水稻叶面积指数的最佳波段。在所选波段中,出现频率最高的波段集中在554、675、723和1633nm附近。其次是444、524、576、594、804、849、974、1074、1219、1510和2194nm。其中大多数具有物理意义,且在光谱已知特征位置有对应物,这表明所选的15个波段在反演水稻叶面积指数方面具有良好的潜力。

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