各向异性反射对基于光谱混合分析的植被盖度估算的影响

光谱混合分析法是解决亚像元植被盖度反演估算问题最行之有效的方法,线性光谱混合分析又是其中最常用的研究手段。传统观点认为基于线性光谱混合分析的植被盖度估算精度主要受多重散射和端元光谱可变性的影响,而且各向异性反射这个地物表面所固有的特性也常常被忽略。研究设计了由寸苔草、沙和水3种地物类别组成的棋盘式混合地物反射率光谱测试方案,在探讨分析传统的多重散射与端元光谱可变性的基础上,进一步分析评价了各向异性反射对基于光谱混合分析的植被盖度估算的影响。结果表明,各向异性反射是不可忽视的,在最小化多重散射及端元光谱可变性的影响后,考虑各向异性反射后寸苔草盖度估算精度显著提升,均方根误差RMSE下降了52%。研究不仅证实了各向异性反射在植被盖度估算问题中的重要性,还印证了其是同种端元类型光谱差异性的另一重要来源,拓宽了端元光谱可变性的范畴,为利用光谱混合分析法更准确地进行植被盖度估算提供了新的思路和方法。

Anisotropic Reflectance Effect on the Spectral Mixture Analysis for Vegetation Coverage Estimation

Spectral mixture analysis (SMA) models are highly effective methods used to deal with sub-pixel vegetation coverage estimation, among which linear spectral mixture analysis (LSMA) is the most commonly used one. However, the precision of vegetation coverage estimation retrieved from LSMA is mainly affected by the multiple scattering and end-member spectral variability. Also, anisotropic reflectance effect (ARE), one of the distinctive and inherent properties of surfaces, is very likely to be ignored. This research conducted in situ spectral experiments by using checkerboard-style mixture design which incorporated three types of surfaces. After discussing and analyzing the traditional multiple scattering and the end-member spectral variability, the effect of anisotropic reflectance on the spectral mixture analysis for vegetation coverage estimation was further evaluated. The results indicated that the impact of ARE cannot be neglected. The Carex duriuscula coverage estimation was more accurate after considering of ARE, when minimizing the effect of the traditional multiple scattering and end-member spectral variability. The root mean square error (RMSE) decreased nearly 52%. These results not only emphasized the importance of integrating ARE into vegetation coverage estimation but also indicated that ARE can be regarded as another significant source of variability within the same end-member class. This study broadens the scope of end-member spectral variability, and may put forward a new thinking and direction for vegetation coverage estimation based on SMA.