基于通用光谱模式分解(UPDM)算法的光谱指数一致性研究
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摘要
由于传感器之间的光谱尺度差异和空间分辨率、成像几何、大气校正精度等因素的共同影响,不同传感器下光谱指数的一致性会受到不同程度的影响。研究选取了HJ和MODIS卫星遥感数据,通过通用光谱模式分解(universal pattern decomposition method,简称UPDM)算法将卫星HJ1A-CCD2(简称HJ)数据进行光谱重构,从而模拟生成对应的MODIS数据,然后分析光谱指数在原始HJ、原始MODIS和模拟MODIS数据之间的差异大小,探讨UPDM算法在不同程度上减小了光谱尺度引起的光谱指数的不确定性,即提高了其一致性。研究结果表明:针对模拟MODIS和原始MODIS数据,光谱指数的确定系数平均值为0. 460 3,差值平均值为0. 811 6,与原始HJ和原始MODIS相比较,一致性有所提高并且差异性变小,减小的差异性即看作是光谱尺度对光谱指数的影响,因此可判断UPDM算法削弱了光谱指数的光谱尺度不确定性,即提高了不同传感器间光谱指数的一致性。研究可为植被理化参量高光谱定量反演模型的构建及精度的提高提供一定的理论基础。
引文
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[11]Liu B,Zhang L,Zhang X,et al. Simulation of EO-1 hyperion data from ALI multispectral data based on the spectral reconstruction approach[J]. Sensors,2009,9(4):3090-3108.
[12]Zhang L,Mitsushita Y,Furumi S,et al. Universality of modified pattern decomposition method for satellite sensors[C]//Asia GIS Conference Publications,2003.
[13]Houborg R,Soegaard H,Boegh E. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data[J]. Remote Sensing of Environment,2007,106(1):39-58.
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