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Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data

文献类型： 外文期刊

作者： Tang Jian-min 1 ; Liao Qin-hong 1 ; Liu Yi-qing 1 ; Yang Gui-jun 2 ; Feng Hai-kuan 2 ; Wang Ji-hua 2 ;

作者机构： 1.Chongqing Univ Art & Sci, Coll Life Sci & Forestry, Chongqing 402160, Peoples R China

2.Beijing Agr Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： Hyperspectral data of CASI; Leaf area index; Vegetation index; Waveband combination; Spatial distribution

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2015 年 35 卷 5 期

页码：

收录情况： SCI

摘要： The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R-2 and RMSE of estimation model were 0.52, 0.45 (p<0.01), respectively. For red-edge vegetation index, CIred (edge) took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI NDSI((569.00), (654.80)) and RSI((597.60), (654.00)) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R-2 of estimation model used NDSI((569.00, 654.80)) was 0.77(p<0.0001), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI((569.00, 664.80)) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy.

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