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摘要： 在遥感对地观测中，受卫星发射、过空时间以及大气等因素的影响，单颗卫星获取的影像难以满足长时间序列的观测需求。因此，定量研究不同卫星平台传感器数据之间的关系是非常必要的。对Sentinel-2A MSI和Landsat-8 OLI传感器之间的定量关系进行了研究，基于两种传感器的3对同日过空的无云影像对，采用样区均值法和全试验区法对二者对应波段的光谱信息进行逐一对比，探求它们之间的定量关系。研究结果表明：Sentinel-2A MSI和Landsat 8 OLI的表观反射率数据总体比较一致，两种方法获得的R2均值分别为0.89 （全试验区法）和0.99 （样区均值法）。但二者也存在着差异，表现在Sentinel-2A MSI的表观反射率总体要比Landsat 8 OLI高约5%，且在不同的波段的表现有所不同。分析表明：二者之间的差异与其光谱响应函数、光谱范围以及试验区土地覆盖类型的不同有关。通过回归分析获得了两种传感器各对应波段数据的转换方程。验证结果表明，转换方程可以显著提高Sentinel-2A MSI和Landsat 8 OLI数据之间的一致性，为二者之间的协同使用提供了可行的方法。 关键词： Sentinel?2A MSI;  Landsat?8 OLI;  表观反射率;  交互对比;  定标     Abstract: The images provided by an individual satellite are difficult to meet the requirement for a long time series observation due to the factors such as satellite operation duration， satellite passage time， atmosphere condition and others. Therefore， it is necessary to quantitatively study the relationship between different satellite sensor data for their collaborative use for a long time series earth observation. In this paper， the quantitative relationship between Sentinel-2A MSI and Landsat-8 OLI sensors’ data was studied. Based on three synchronous， cloud-free image pairs of the two sensors， the corresponding bands of the two sensors were compared band-by-band by using the Region Of Interest （ROI） method and the whole test area method to explore the quantitative relationship between them. The results show that the Top Of Atmospheric （TOA） reflectance data of Sentinel-2A MSI and Landsat-8 OLI are generally consistent， and the mean R2 values obtained via the two methods are 0.89 （whole test area method） and 0.99 （ROI method）， respectively. However， the differences between the two sensors’ data have also been revealed. The total TOA reflectance of Sentinel-2A MSI is generally about 5% higher than that of Landsat-8 OLI， which， however， is different in different bands. The analysis shows that this difference is due to the two sensors’ differences in spectral response function and spectral range， as well as the difference in land cover type of the test areas. By regression analysis， the data conversion equations of each band between the two sensors are obtained. The validation results show that the conversion equations can significantly improve the consistency of Sentinel-2A MSI and Landsat-8 OLI data， providing a feasible method for the collaborative use of the two sensors’ data.

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