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On the Interplay Between Ocean Color Data Quality and Data Quantity: Impacts of Quality Control Flags

机译:关于海洋颜色数据质量和数据数量之间的相互作用:质量控制标志的影响

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摘要

Nearly all calibration/validation activities for the satellite ocean color missions have focused on data quality to produce data products of the highest quality (i.e., science quality) for climate-related research. Little attention, however, has been paid to data quantity, particularly on how data quality control during data processing impacts downstream data quality and data quantity. In this letter, we attempt to fill this knowledge gap using measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). For this sensor, the same level-1B data are processed independently using different quality control methods by NASA and NOAA, respectively, allowing for an in-depth evaluation of the interplay between data quantity and quality. The results indicate that the methods to identify stray light and sun glint are the two primary quality control procedures affecting data quantity, where the criteria for flagging pixels "contaminated" by stray light and sun glint may be relaxed in the NASA ocean color data processing to increase data quantity without compromising data quality.
机译:几乎所有卫星海洋彩色任务的校准/验证活动都集中在数据质量上,为气候相关的研究生产最高质量(即科学质量)的数据产品。但是,已经注意到数据数量很少,特别是关于数据处理期间的数据质量控制如何影响下游数据质量和数据量。在这封信中,我们试图使用来自Suomi National Orbiting Partnership(SNPP)的可见红外成像辐射计套件(VIIRs)的测量来填充这种知识差距。对于该传感器,通过NASA和NOAA分别使用NASA和NOAA的不同质量控制方法独立地处理相同的电平-1B数据,允许深入评估数据量和质量之间的相互作用。结果表明,识别杂散光和太阳闪光的方法是影响数据量的两个主要质量控制程序,其中在NASA海洋颜色数据处理中可以放松杂交灯和太阳闪光的像素“被污染”的像素“污染”的标准增加数据量而不会影响数据质量。

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