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

机译:Hyperion EO-1图像的季节性时间序列的大气校正和红边拐点计算

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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:这项研究涵盖了来自芬兰南部海蒂亚拉(61°51′N,24°17′E)的五个时间序列的Hyperion EO-1图像的季节序列的预处理和大气校正。时间序列的时间范围为2010年5月5日至2010年7月11日,涵盖了大部分生长期和植被反射率的季节性变化。时间序列的大气校正通过光谱超立方体的快速视线大气分析(FLAASH)和大气校正(ATCOR)算法进行比较。两种算法在Hyperion影像上均表现良好。分析了不同的红边拐点(REIP)计算方法,以确定它们在Hyperion图像中的适用性。 REIP是使用四点插值,拉格朗日插值和五阶多项式拟合计算的。由于红色边缘的动态,多项式拟合被视为计算REIP的最佳方法。 REIP与叶面积指数(LAI)的相关性不强,但与林下REIP的相关性更强。

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