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INTEGRATION OF SENTINEL-2 AND LANDSAT-8 DATA FOR SURFACE REFLECTANCE TIME-SERIES ANALYSIS

机译:Sentinel-2的集成和Landsat-8表面反射时间序列分析的数据

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摘要

Integration of Sentinel-2 and Landsat-8 imagery is a key factor to provide earth observation data at a global scale with higher temporal resolution. Integration of data from two sensors is possible with the consistent harmonized data framed in common reference and processing, which can be used for comparing geophysical surface characteristics. This study focuses on the analysis of the atmospheric correction methods available for both Landsat-8 and Sentinel-2 products to convert the top of the atmosphere to the bottom of atmosphere reflectance. Other investigations (De Keukelaere, 2018) carried out similar analyses focusing on data acquired over water, while this study emphasises the analyses over land covers. Two processing algorithms iCOR and Sen2COR are utilized to perform atmospheric corrections, and results are statistically and visually compared. Comparisons based on same images processed with different algorithms show very strong correlation for some classes (urban: 0.99), while correlation values around 0.85 were achieved between images from different sensors.
机译:Sentinel-2和Landsat-8图像的集成是通过较高的时间分辨率以全球范围提供地球观测数据的关键因素。可以使用共同参考和处理中的一致谐波数据来集成来自两个传感器的数据,这可以用于比较地球物理表面特征。本研究的重点的同时适用于陆地卫星8和Sentinel-2产品大气校正方法分析到大气层的顶端转换到大气反射的底部。其他调查(De Keukelaere,2018)进行了类似的分析,重点是在水中获得的数据,而这项研究强调了对陆地覆盖物的分析。两个处理算法ICOR和SEN2COR用于进行大气校正,并且结果在统计上和视觉上进行比较。基于用不同算法处理的相同图像的比较显示了某些类(Urban:0.99)的非常强烈的相关性,而来自不同传感器的图像之间的图像之间可以实现0.85左右的相关值。

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