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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM + top of atmosphere spectral characteristics over the conterminous United States
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Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM + top of atmosphere spectral characteristics over the conterminous United States

机译:Sentinel-2a和2b Msi,Landsat-8 Oli和Landsat-7 Etm +大气谱特性的经验交叉传感器比较孔雀体美国的顶部

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Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites-7 and 8 provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM +. Despite differences in their spatia
机译:遥感景观监测方法经常受益于密集的时间序列的观察。为了增强这些时间序列,需要集成来自多个卫星系统的数据。 Landsat图像数据是一个有价值的30米分辨率的空间信息来源,用于随着时间的推移评估森林条件。共同运营Landsat卫星-7和8在赤道上提供8天的重新探。这种适度的时间频率提供了检测年度大面积突变覆盖变化的基本信息。然而,衡量微妙和短暂的陷入困境变化的能力受到在时间点的关键点的岩石图像中的差距挑战。第一个Sentinel-2卫星特派团于2015年由欧洲航天局推出。这种温和的数据流为补充Landsat数据记录提供了机会。本研究的目的是评估将大气顶部与归档的谷歌地球发动机计算环境中归档的大气层和Sentinel 2图像数据集成的可能性。在本文中,我们评估了六个频带的绝对和比例差异,在六个频段具有可比的光谱响应函数和Sentinel-2多谱仪和Landsat运算陆地成像器和增强的专题映射器加上图像的空间分辨率。我们评估了在锥形美国跨越10,000多个图像对之间的绝对差异度量和主要轴线性回归和当前交叉传感器转换模型的差异。主要轴线性回归结果表明,由于Landsat传感器彼此相互作用,Sentinel MSI数据与两种类型的Landsat图像数据相当。在MSI和LANDSAT光谱值之间获得0.0121至0.0398的根平均方偏差(RMSD)值,并且在OLI和ETM +之间获得0.0124和0.0372的RMSD值。尽管他们的太空差异

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