Generation of consistent multi-sensor datasets is critical to the assessment of long-term global changes using satellite-borne instruments. Recent research suggests, however, that a fundamental assumption in satellite ocean color data processing concerning the calibration of the long near infrared band (i.e., 865 nm for MODIS) may introduce sensor-specific biases in space and/or time, which may also contribute to cross-sensor inconsistency in the derived reflectance data products. As such, it is necessary to assess the calibration of this band across sensors – performed here for MODIS/Aqua and VIIRS/SNPP using ‘simultaneous same view’ matchups (SSV; similar to simultaneous nadir overpass, but allowing for non-nadir measurements). Towards that end, we assess geometric, temporal, and spatial homogeneity metrics to identify SSVs, and develop a band-shifting approach applicable within standard satellite data processing routines to resolve expected spectral differences in the radiometry. We find top-of-atmosphere (TOA) radiance data from VIIRS/SNPP long near infrared band to be approximately 3% higher than the corresponding MODIS/A data. With the expectation that cross-calibrating the NIR_L should improve cross-sensor continuity of downstream geophysical products (e.g., chlorophyll-a), we reprocessed VIIRS data using updated calibration coefficients. While we noticed many minor improvements in cross-sensor continuity in such data products, large-scale geographic and temporal biases between these two datasets still remain. These discontinuities may be the result of disparate errors in polarization correction or atmospheric correction, both of which are modulated by radiant path geometry.

一致的多传感器数据集的生成对于使用星载仪器评估长期全球变化至关重要。但是，最近的研究表明，卫星海洋颜色数据处理中有关长近红外波段（即MODIS的865 nm）校准的基本假设可能会在空间和/或时间上引入特定于传感器的偏差，这也可能有助于在导出的反射率数据产品中出现跨传感器不一致的情况。因此，有必要评估跨传感器的该频段的校准–在此处使用“同时相同视图”匹配（SSV；类似于同时的最低点立交，但允许非最低点测量）针对MODIS / Aqua和VIIRS / SNPP执行此操作。为此，我们评估几何，时间和空间同质性指标以识别SSV，并开发一种适用于标准卫星数据处理程序的移频方法，以解决辐射测量中预期的光谱差异。我们发现来自VIIRS / SNPP的长近红外波段的大气（TOA）辐射数据比相应的MODIS / A数据高大约3％。期望可以对NIR进行交叉校准_L应该改善下游地球物理产品（例如，叶绿素a）的跨传感器连续性，我们使用更新的校准系数对VIIRS数据进行了重新处理。尽管我们注意到此类数据产品在跨传感器连续性方面有许多小的改进，但仍保留了这两个数据集之间的大规模地理和时间偏差。这些不连续性可能是极化校正或大气校正中不同误差的结果，这两种误差均由辐射路径几何形状进行调制。