Seasonal bias in global ocean color observations
Seasonal bias in global ocean color observations
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DOI：
10.1364/ao.426137
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发表时间：
2021-08-10
期刊：
影响因子：
1.9
通讯作者：
Behrenfeld, M. J.
中科院分区：
文献类型：
--
作者：
Bisson, K. M.;Boss, E.;Behrenfeld, M. J.
在本研究中，我们确定了海洋颜色卫星衍生遥感反射率（R-rs(lambda)）的季节性偏差；sr(-1))在海洋颜色验证地点，海洋光学浮标。R-rs（lambda）的季节偏差在所有被检测的海洋彩色卫星中都不同程度地存在，包括可见光红外成像辐射计套件、海洋观测宽视场传感器和中分辨率成像光谱仪。相对比偏差具有谱依赖性。由R-rs（lambda）衍生的产品受到不同程度偏差的影响，颗粒后向散射在一年内变化高达50%，叶绿素在一年内变化高达25%，浮游植物或溶解物质的吸收变化高达15%。在区域和全球尺度上，利用Argo浮标和云-气溶胶激光雷达的正交偏振仪和红外探路者卫星数据，广泛证实了R-rs（lambda）偏置在衍生产品中的传播。海洋颜色的人工季节性在低生物量地区（即亚热带环流）很突出，而在高生物量地区则不容易辨别。虽然我们已经排除了几个可能导致R-rs（lambda）偏差的候选因素，但关于大气校正的潜在贡献仍然存在悬而未决的问题。具体来说，我们提供的证据表明，水生双向反射分布函数可能在一定程度上导致观测到的季节偏差，但这并不排除气溶胶估计的额外影响。我们的研究强调了大气校正方案在引入R-rs（lambda）偏差方面的贡献，我们建议进行更多的模拟来辨别这些影响R-rs（lambda）偏差。需要社区努力找到季节性偏差的根本原因，因为在实施解决方案之前，所有过去、现在和未来的数据都受到或将受到影响。(C) 2021美国光学学会。
In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (R-rs(lambda); sr(-1)) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in R-rs(lambda) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, andModerate Resolution Imaging Spectrometer. The relative bias in Rrs has spectral dependence. Products derived from R-rs(lambda) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of R-rs(lambda) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in R-rs(lambda) there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in R-rs(lambda), and we recommend more simulations to discern these influence R-rs(lambda) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented. (C) 2021 Optical Society of America.