基于高光谱数据的叶面积指数遥感反演

Leaf area index retrieval from remotely sensed hyperspectral data

  • 摘要: 文中耦合叶片辐射传输模型(PROSPECT)和冠层辐射传输模型(SAILH),基于高光谱载荷通道设置,模拟高光谱冠层反射率数据;利用模拟数据深入分析了不同植被指数与叶面积指数之间的敏感性;通过敏感性分析发现改进型叶绿素吸收植被指数(MCARI2)具备抗土壤背景因素的影响能力,而且对叶面积指数较为敏感,因此该研究建立植被指数MCARI2 与叶面积指数之间的经验统计模型,并用于高光谱数据进行叶面积指数反演;最后利用飞行同步测量的叶面积指数对反演模型进行精度分析。结果表明:相比实测叶面积指数,文中建立的反演模型约低估0.42,该反演模型能够较好的反映出地物真实叶面积指数。

     

    Abstract: An experimental leaf area index (LAI) retrieval model was proposed with the aid of a leaf- radiative transfer model (PROSPECT) and a canopy bidirectional reflectance model (SAILH) to simulate the canopy reflectance in this paper. Then, the vegetation indices (VIs) were introduced, and the sensitivities were analyzed between LAI and VIs, soil background. Based on the sensitivity analysis, a modified chlorophyll ratio index II (MCARI2) was proposed by Haboudane et al. (2004) was used to build the LAI retrieval model, because it is rather sensitive to the LAI and insensitive to soil background. Finally, the retrieval model proposed was performed to estimate LAI from the hyperspectral data. Compared with the ground-measured LAI, the LAI retrieved from hyperspectral data underestimate approximately 0.42.

     

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