摘要： 水体的遥感反射比光谱（Rrs(λ)）是海洋水色遥感反演海洋生物地球光学参数的关键，其定义是离水辐亮度与恰好水面之上的向下辐照度之比。海洋水色卫星传感器接收到的总信号中90%是大气的贡献，海洋水体贡献的离水辐亮度不足10%，因此对接收的信号进行大气校正获得高精度的水体遥感反射比信号是海洋光学遥感的关键技术之一。基于大量高质量的现场高光谱遥感反射比数据的基础上建立的Rrs(λ)光谱数据的质量评价体系QA(quality assurance)，可以通过计算Rrs的得分情况（QA score）很好地识别出有问题或可能错误的Rrs(λ)光谱。GOCI(geostationary ocean color imager)是搭载在全球第一颗对地静止卫星COMS(communication ocean and meteorological satellite)上的主要传感器，由韩国海洋卫星中心（KOSC）发射，其高观测频次(8景观测数据/天)使生物地球化学参数的日变化监测成为可能。KOSC研发了GDPS (GOCI data processing system)软件专门用于GOCI数据处理，包括大气校正。到目前为止已为全球用户免费提供GDPS1.1, GDPS1.2, GDPS1.3, GDPS1.4, GDPS1.4.1, GDPS2.0六个版本。应用QA Score评价体系对于GDPS1.2, GDPS1.3, GDPS1.4.1, GDPS2.0四个版本在黄海海域处理得到的GOCI遥感反射比光谱数据的质量进行了评比。结果发现GDPS1.2的Rrs数据被视为无效的数据量明显大于GDPS1.3, GDPS1.4.1和GDPS2.0的处理结果；GDPS2.0的Rrs数据QA得分情况要差于GDPS1.2，GDPS1.3和GDPS1.4.1；GDPS1.3和GDPS1.4.1的数据处理结果基本相同，这与GDPS1.4在GDPS1.3的基础上只进行了软件模块化优化处理且修复了一些小问题的结果相吻合。基于该研究，黄海海域使用GOCI Rrs数据时，如果Rrs波段比是首要考虑因素（如反演叶绿素a浓度）且对有效数据数量要求不高，可以使用GDPS1.2版本进行大气校正；如果更关心的是某个波段Rrs值，则使用GDPS2.0进行大气校正更合适。
关键词：海洋水色遥感；GOCI；GDPS；黄海；遥感反射比
Abstract：Spectral remote-reflectance sensing (Rrs(λ)) is significant to the retrieval of ocean bio-optical properties from ocean color. Rrs(λ) is defined as the ratio of water-leaving radiance to the downward irradiance just above the water surface. About 90% of the total signal received by ocean color sensor is contributed by the atmosphere, while only less than 10% is contributed by ocean water. Therefore, the process of atmospheric correction is an essential part of ocean color remote sensing to get accurate remote-sensing reflectance. Based on a large number of high-quality on-site hyper-spectral remote-reflectance sensing data, a QA (Quality Assurance) evaluation system for Rrs data has been established to diagnose problematic or possibly wrong Rrs spectra by calculating Rrs QA scores. GOCI (Geostationary Ocean Color Imager) is the main sensor on the world’s first geostationary satellite COMS (Communication Ocean and Meteorological Satellite), launched by the Korea Ocean Satellite Center (KOSC). Its high observation frequency (8 observation survey Data/day) makes it possible to monitor daily changes in biogeochemical parameters. KOSC developed GDPS (GOCI Data Processing System) software for GOCI data processing, within which atmospheric correction algorithm is integrated. The versions GDPS1.1, GDPS1.2, GDPS1.3, GDPS1.4, GDPS1.4.1 and GDPS2.0 have been published and provided for free. In this paper, a QA Score evaluation system was applied to evaluate the quality of GOCI remote-sensing reflectance product processed by GDPS1.2, GDPS1.3, GDPS1.4.1 and GDPS2.0 in the China Yellow Sea. It showed that the amount of valid Rrs data from GDPS1.2 was significantly less than that either from GDPS1.3, GDPS1.4.1 or GDPS2.0. The QA score of Rrs data from GDPS2.0 was lower than that of GDPS1.2, GDPS1.3 or GDPS1.4.1. It makes sense that the QA score of Rrs from GDPS1.3 and GDPS1.4.1 are the same. Because compared to GDP1.3, only software modularization is optimized, and some minor problems are fixed in GDPS1.4. Based on our results, when applying GOCI Rrs data to the Yellow Sea, it is suggested that if Rrs ratio is the first-order parameter (i. e., retrieving chl-a concentration) and there is no requirement of valid data amount, atmospheric correction codes in GDPS 1.2 can be selected to used to get Rrs. If Rrs data at a certain wavelength is concerned, GDPS2.0 is more suitable for processing GOCI data.
Key words：Ocean color remote sensing；GOCI；GDPS；Yellow Sea；Remote-reflectance sensing
刘晓燕，杨 倩，刘巧君. 基于QA Score的GDPS各个版本在黄海海域GOCI数据处理中的适应性分析[J]. 光谱学与光谱分析, 2021, 41(07): 2233-2239.
LIU Xiao-yan, YANG Qian, LIU Qiao-jun. Adaptability Analysis of Various Versions of GDPS Based on QA Score for GOCI Data Processing in the Yellow Sea. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2233-2239.
[1] Sathyendranath Shubha, Arnone Robert, Babin Marcel. Reports of the International Ocean-Colour Coordinating Group, No. 3, 2000. 140
[2] Morel A, Prieur L. Limnol. Oceanogr, 1977, 22: 709.
[3] Yang Qian, Du Libin, Liu Xiaoyan, et al. Proceedings of SPIE—The International Society for Optical Engineering, 2014. 9261.
[4] Kim W, Moon J, Park Y, et al. Remote Sens. Environ., 2016, 184: 482.
[5] Lee Z P, Carder K L, Arnone R. Appl. Opt., 2002, 41(27): 5755.
[6] Wei J, Lee Z P. Appl. Opt., 2015, 54(4): 636.
[7] HUANG Xiao-can, ZHU Jian-hua, LI Tong-ji(黄筱灿, 朱建华, 李铜基). Journal of Ocean Technology(海洋技术学报), 2018, 37(6): 118.
[8] Gordon H R. Applied Optics, 1978, 17(10): 1631.
[9] Gordon H R, Wang Menghua. Applied Optics, 1994, 33(3): 443.
[10] Antoine D, Morel A. International Journal of Remote Sensing, 1999, 20(9): 1875.
[11] Antoine D, Morel A. Applied Optics, 1998, 37(12): 2245.
[12] He Xianqiang, Bai Yan, Pan Delu. Optics Express, 2012, 20(18): 20754.
[13] Wang Menghua, Shi Wei. Applied Optics, 2007, 46(9): 1535.
[14] Ruddick K G, Ovidio F, Rijkeboer M. Applied Optics, 2009, 39(6): 897.
[15] Moore G F, Aiken J, Lavender S J. International Journal of Remote Sensing, 1999, 20(9): 1713.
[16] Ahn J H, Park Y J, Ryu J H, et al. Ocean Science Journal, 2012, 47(3): 247.
[17] Doerffer R, Schiller H. GKSS Research Center, 21502 Geesthacht, 2008, 28(2): 517.
[18] Gordon H R, Du Tao, Zhang Tingmin. Applied Optics, 1997, 36(33): 8670.
[19] Garver S A, Siegel D A. Journal of Geophysical Research, 1997, 102(C8): 18607.
[20] Bailey S W, Werdell P J. Remote Sens. Environ., 2006, 102(1-2): 12.
[21] Zibordi G, Berthon J F, Melin F, et al. Remote Sens. Environ., 2009a, 113: 2574.
[22] Wei Jianwei, Lee Zhongping, Shang Shaoling. Journal of Geophysical Research: Oceans, 2016, 121(11): 8189.
[23] Kruse F A, Lefkoff A B, Boardman J B，et al. Remote Sens. Environ., 1993, 44: 145.
[24] ARyu J H, Han H J, Cho S. Ocean Science Journal, 2012, 47(3): 223.
[25] Jae-Hyun Ahn, Young-Je Park, Wonkook Kim. Opt Express, 2016, 24(26): 29659.
