Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different EOS requires compensating for atmospheric and view angle effects before integrating comparable surface reflectance (SR) values. The objectives of this study were i) to assess how different atmospheric constituents affect the atmospheric correction results in Sentinel-2 and WorldView-3 imagery, ii) to establish a relationship with field spectra measurements, and iii) to develop an empirical approach to ensure that SR values extracted from different EOS can be normalized for use in monitoring vegetation and land cover status. We compared surface reflectance values derived from Sentinel-2 images corrected with Sen2Cor, MODTRAN or FLAASH atmospheric correction approaches for the visible-to-near infrared regions. Additionally, this information was compared to SR values extracted from WorldView-3 imagery acquired from the same dates and location (Central Spain) and corrected with MODTRAN and FLAASH approaches. Assessment of the atmospheric correction was conducted by comparing satellite image SR with ground-truth spectra acquired with a FieldSpec hand-held spectroradiometer. The results emphasized the importance of using common atmospheric parameters collected from ancillary data sources (i.e. MODIS Atmosphere & Land products) to ensure a reliable SR comparison. When compared to field-collected spectral data, SR from corrected Sentinel-2 push-broom imagery showed a reliable match (<4% difference in the visible bands and <0.52% difference in the near infrared bands). However, SR imagery from the pointable WorldView-3 instrument showed significant deviation, likely resulting from the effects of steep off-nadir acquisition angles (24.6° to 39.1°) combined with surface anisotropy. The magnitude and sign of the deviation in SR differed depending on the vegetation type, wavelength and sun-surface-sensor geometry. Therefore, it was necessary to account for angular effects to ensure reliable comparisons of imagery from the different EOS. In this study, an empirical angular correction approach was developed based on calibrating each WorldView-3 band against the ground-truth spectra. This correction allowed for the accurate signal normalization of WorldView-3 and Sentinel-2 imagery SR in the visible-to-near infrared regions.

遥感卫星地球观测系统（EOS）提供了多种产品，可在不同的空间和光谱分辨率下监视地球表面过程。结合来自高中空间分辨率图像的信息对于监视农田，草地，森林和其他自然环境中的地被植物和植被状态非常有价值。但是，来自不同EOS的耦合信息需要在整合可比较的表面反射率（SR）值之前补偿大气和视角影响。这项研究的目的是：i）评估不同的大气成分如何影响Sentinel-2和WorldView-3影像中的大气校正结果； ii）建立与野外光谱测量之间的关系，iii）开发一种经验方法，以确保将从不同EOS提取的SR值标准化，以用于监测植被和土地覆盖状况。我们比较了从Sen2Cor，MODTRAN或FLAASH大气校正方法校正的Sentinel-2图像得出的表面反射率值，用于可见到近红外区域。此外，将此信息与从相同日期和位置（西班牙中部）获取的WorldView-3影像中提取的SR值进行了比较，并使用MODTRAN和FLAASH方法进行了校正。通过将卫星图像SR与用FieldSpec手持式光谱仪获得的地面光谱进行比较，进行大气校正的评估。结果强调了使用从辅助数据源（即：MODIS大气和陆地产品）以确保可靠的SR比较。当与现场收集的光谱数据进行比较时，来自校正后的Sentinel-2推扫扫帚图像的SR显示出可靠的匹配（可见波段的差异小于4％，近红外波段的差异小于0.52％）。但是，可指向的WorldView-3仪器的SR图像显示出明显的偏差，这可能是由于陡峭的离天底采集角（24.6°至39.1°）以及表面各向异性的影响。SR偏差的大小和符号取决于植被类型，波长和太阳表面传感器的几何形状。因此，有必要考虑角度效应，以确保可靠地比较来自不同EOS的图像。在这个研究中，基于对每个WorldView-3波段相对于地面真实光谱进行校准的基础上，开发了一种经验性的角度校正方法。通过进行此校正，可以对可见到近红外区域中的WorldView-3和Sentinel-2图像SR进行精确的信号归一化。