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Atmospheric correction of a seasonal time series of Hyperion EO-1 images and red edge inflection point calculation

机译:大气校正Hyperion EO-1图像和红色拐点计算的季节性时间序列

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This study covers the preprocessing and atmospheric correction of a seasonal time series five Hyperion EO-1 images from Hyytiälä, Southern Finland (61° 51′N, 24° 17′E). The time series ranges from May 5th 2010 to July 11th 2010, covering much of the growing season and the seasonal changes in vegetation reflectance. Atmospheric correction of the time series was done with Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and ATmospheric CORrection (ATCOR) algorithms for comparison. Both algorithms performed well with Hyperion imagery. Different red edge inflection point (REIP) calculation methods were analyzed to determine their applicability for Hyperion imagery. REIP was calculated using four-point interpolation, Lagrangian interpolation, and fifth order polynomial fitting. Due to the dynamics of the red edge, polynomial fitting was seen as the best method for calculating the REIP. REIP did not correlate strongly with Leaf Area Index (LAI) but a stronger correlation was observed with understory REIP.
机译:本研究包括季节性时间序列的预处理和大气校正5海波EO-1从Hyytiälä图像,芬兰南部(61°51'N,24°17'E)。时间序列的范围从2010年5月5日至2010年7月11日,覆盖生长季节多,植被反射率的季节性变化。时间序列的大气校正与光谱超立方体(FLAASH)和大气校正(ATCOR)算法的快速线的视距大气进行分析比较。这两种算法与海波龙的图像表现良好。分析不同红边拐点(REIP)的计算方法,以确定其为海波图像适用性。 REIP使用四点内插,内插拉格朗日,和五阶多项式拟合计算。由于红边的动态,多项式拟合被视为用于计算REIP的最佳方法。 REIP没有与叶面积指数(LAI)很强的相关性,但与林下REIP观察到较强的相关性。

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