浏览全部资源
扫码关注微信
Published： 07 January 2023 ，
扫 描 看 全 文
韩冰，贾迪，高飞，郭凯，朱建华，李铜基，马超飞，刘建强，Giuseppe Zibordi.2023.HY-1C卫星COCTS L2A级产品在中国和欧洲近海的真实性检验.遥感学报，27（1）： 26-42
Han B， Jia D， Gao F， Guo K， Zhu J H， Li T J， Ma C F， Liu J Q and Zibordi G. 2023. Validation of L2A operational products of COCTS onboard HY-1C satellite across coastal waters in China and Europe. National Remote Sensing Bulletin， 27（1）：26-42
随着HY-1C卫星于2018年9月成功发射，中国海洋水色遥感进入业务化全球观测时代。海洋环境观测、生态环境监测以及海洋防灾减灾等都对海洋水色遥感提出了较高的定量化要求。本文基于国际通用的时空匹配方法，利用2018年和2020年海洋卫星在轨测试海上试验以及在中国东海、欧洲亚得里亚海近岸海域部署的自动太阳光度计（SeaPRISM）观测数据，对HY-1C卫星搭载的水色水温扫描仪（COCTS）的L2A级业务化产品（包括遥感反射率和气溶胶光学厚度）进行了真实性检验。研究发现，SeaPRISM自动测量能够提升数据获取效率和观测数据时间跨度，有助于更好刻画水色遥感产品误差的时间分布规律。检验结果表明，COCTS的遥感反射率与现场实测数据具有较好的一致性，但匹配数据的离散度较高，其蓝光波段、绿光波段和红光波段的遥感反射率在不同海域与实测数据的平均相对百分比偏差（RPD）和平均绝对百分比偏差（APD）分别为-5.6%和46.8%、11.8%和53.0%、101.4%和173.9%，而其气溶胶光学厚度的在不同海域与实测数据的RPD和APD分别为-14.2%和79.5%；COCTS遥感反射率光谱形状与实测数据一致性较高，但在不同海域表现出较大差异：中南海海域的谱形一致性非常高，但呈显著高估特点；中国东海海域的谱形相似度高，但存显著的低估；而欧洲亚得里亚海海域的谱形差异较大，即在蓝光波段呈现高估特点，而在绿光和红光波段呈现低估特点。对比同期的HY-1C卫星COCTS L2A及产品与AQUA卫星MODIS二级产品的检验结果发现，后者在遥感反射率估算精度和光谱谱形相似度方面表现更好，而在气溶胶光学厚度定量反演方面则没有表现出显著优势。本文的研究认为COCTS数据虽然已经在多个应用领域发挥了重要作用，但其业务化产品的定量化精度和水平仍有较大提升空间，至少L2A级产品是如此。
Since the successful launch of HY-1C in September
2018
China has entered a new era of operational global ocean color remote sensing. As one of its three major payloads
the Chinese Ocean Color and Temperature Scanner (COCTS) measures the radiances scattered from the atmosphere
ocean and sea surface in eight visible and near-infrared bands and two thermal infrared bands across a swath of over 2500 km. As many businesses such as marine environment observation
marine ecological monitoring and marine disaster prevention and mitigation are putting higher demands on quantitative ocean color products
we here validate operational L2A products retrieved from COCTS data across coastal waters across China and Europe
and also compare them with widely accepted L2 products of MODIS onboard AQUA satellite. Validation practice uses in-situ data obtained in field campaigns in the East and South China Sea in 2018 and 2020
and those derived by automated sun-photometers (SeaPRISM
CIMEL Inc.) deployed in the East China Sea and in the Adriatic Sea in Europe. Confined by that SeaPRISM can only produces remote-sensing reflectance and aerosol optical thickness directly
we only validate operational L2A products provided by COCTS
which include Remote Sensing Reflectance and Aerosol Optical Thickness. Match-up practice follows those protocols widely accepted in the ocean color community. It is found that SeaPRISM can provide
in-situ
data in a more efficient way and the data covers a larger temporal range
which facilitates characterizing temporal and spatial uncertainty of ocean color products. Validation results show that both remote-sensing reflectance and aerosol optical products of COCTS L2A products agree well with in-situ measurements
but their comparison shows larger deviation. The average relative percentage difference (RPD) between COCTS L2A product and field measurement across various coastal waters are -5.6%
11.8% and 101.4% in blue
green and red bands respectively
while the average Absolute Percentage Ddifference (APD) are 46.8%
53.0% and 173.9% respectively. Meanwhile
for aerosol optical thickness
the average RPD and APD between COCTS L2A product and field measurement across these waters are -14.2% and 79.5%
respectively. Remote sensing reflectance of COCTS shows similar spectral shape to that of in-situ data
but their similarity varies across different waters. Specially
remote sensing reflectance in the South China Sea demonstrates similar spectral shape but shows large overestimation; high spectral shape similarity exists in the East China Sea but COCTS gives underestimation; there appears large deviation in spectral shape in the Adriatic Sea
namely
overestimation in blue but underestimation in the blue and red. As compared with the validation results of MODIS L2 products following same criteria
it is found that MODIS performs better than COCTS in quantitative retrieval of remote-sensing reflectance
but shows no obvious advantage in quantitative retrieval of aerosol optical thickness. Although HY-1C COCTS operational products have contributed to wide range of applications
this practice suggests there is still great space in improving quantitative retrieval of ocean color products
at least L2A products.
HY-1C卫星COCTSL2A级产品真实性检验中国近海欧洲近海遥感反射率气溶胶光学厚度
HY-1C SatelliteCOCTSL2A productvalidationcoastal waters around Chinacoastal waters around Europeremote-sensing reflectanceaerosol optical thickness
Bailey S W and Werdell P J. 2006. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment, 102(1/2): 12-23 [DOI: 10.1016/j.rse.2006.01.015http://dx.doi.org/10.1016/j.rse.2006.01.015]
Gilerson A, Herrera E, Klein Y, Foster R, Gross B, Arnone R and Ahmed S. 2017. Characterization of aerosol parameters over ocean from the Ocean Color satellite sensors and AERONET-OC data//Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions, 10422. SPIE: 79-90 [DOI: 10.1117/12.2279150http://dx.doi.org/10.1117/12.2279150]
Glover D M, Doney S C, Oestreich W K and Tullo A W. 2018. Geostatistical analysis of mesoscale spatial variability and error in SeaWiFS and MODIS/Aqua global ocean color data. Journal of Geophysical Research: Oceans, 123(1): 22-39 [DOI: 10.1002/2017JC013023http://dx.doi.org/10.1002/2017JC013023]
Gordon H R and Morel A Y. 1983. Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review. New York: Springer-Verlag [DOI: 10.1007/978-1-4684-6280-7http://dx.doi.org/10.1007/978-1-4684-6280-7]
Gordon H R and Wang M H. 1994. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. Applied Optics, 33(3): 443-452 [DOI: 10.1364/AO.33.000443http://dx.doi.org/10.1364/AO.33.000443]
Groom S, Sathyendranath S, Ban Y, Bernard S, Brewin R, Brotas V, Brockmann C, Chauhan P, Choi J K, Chuprin A, Ciavatta S, Cipollini P, Donlon C, Franz B, He X Q, Hirata T, Jackson T, Kampel M, Krasemann H, Lavender S, Pardo-Martinez S, Mélin F, Platt T, Santoleri R, Skakala J, Schaeffer B, Smith M, Steinmetz F, Valente A and Wang M H. 2019. Satellite ocean colour: current status and future perspective. Frontiers in Marine Science, 6: 485 [DOI: 10.3389/fmars.2019.00485http://dx.doi.org/10.3389/fmars.2019.00485]
Han B, Zhu J H, Li T J, Li J, Jia D, Guo K, Zhifeng L and Yang A A. 2019. Preliminary validation of sentinel 3A OLCI bio-optical products in South China Sea//IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 7877-7880 [DOI: 10.1109/IGARSS.2019.8898741http://dx.doi.org/10.1109/IGARSS.2019.8898741]
He M J, He S Y, Zhang X D, Zhou F and Li P L. 2021. Assessment of normalized water-leaving radiance derived from GOCI using AERONET-OC data. Remote Sensing, 13(9): 1640 [DOI: 10.3390/rs13091640http://dx.doi.org/10.3390/rs13091640]
Hlaing S, Gilerson A, Foster R, Wang M H, Arnone R and Ahmed S. 2014. Radiometric calibration of ocean color satellite sensors using AERONET-OC data. Optics Express, 22(19): 23385-23401 [DOI: 10.1364/OE.22.023385http://dx.doi.org/10.1364/OE.22.023385]
IOCCG. 2000. Remote sensing of ocean colour in coastal, and other optically-complex, waters//Sathyendranath S ed. Reports of the International Ocean-Colour Coordinating Group, No. 3. Dartmouth: IOCCG [DOI: 10.25607/OBP-95http://dx.doi.org/10.25607/OBP-95]
Jamet C, Loisel H, Kuchinke C P, Ruddick K, Zibordi G and Feng H. 2011. Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements. Remote Sensing of Environment, 115(8): 1955-1965 [DOI: 10.1016/j.rse.2011.03.018http://dx.doi.org/10.1016/j.rse.2011.03.018]
Jia D, Gao F, Han B, Li T J, Hong B, Zhu J H and Li J. 2020. Quality control technology and practice for automatic observation of atmospheric optical parameters on the sea// Second Target Recognition and Artificial Intelligence Summit Forum, 11427. SPIE: 1033-1048 [DOI: 10.1117/12.2553188http://dx.doi.org/10.1117/12.2553188]
Joo H T, Son S H, Park J W, Kang J, Jeong J Y, Lee C, Kang C K and Lee S. 2015. Long-term pattern of primary productivity in the East/Japan Sea based on ocean color data derived from MODIS-aqua. Remote Sensing, 8(1): 25 [DOI: 10.3390/rs8010025http://dx.doi.org/10.3390/rs8010025]
Lawson A, Bowers J, Ladner S, Crout R, Wood C, Arnone R, Martinolich P and Lewis D. 2021. Analyzing satellite ocean color match-up protocols using the Satellite Validation Navy Tool (SAVANT) at MOBY and two AERONET-OC sites. Remote Sensing, 13: 2673 [DOI: 10.3390/rs13142673http://dx.doi.org/10.3390/rs13142673]
Liu J Q, Zeng T, Liang C, Zou Y R, Ye X M, Ding J, Zou B, Shi L J and Guo M H. 2020. Application of Haiyang-1C satellite data in the monitoring of natural hazard and disaster. Satellite Application, (6): 26-34
刘建强, 曾韬, 梁超, 邹亚荣, 叶小敏, 丁静, 邹斌, 石立坚, 郭茂华. 2020. 海洋一号C卫星在自然灾害监测中的应用. 卫星应用, (6): 26-34 [DOI: 10.3969/j.issn.1674-9030.2020.06.008http://dx.doi.org/10.3969/j.issn.1674-9030.2020.06.008]
Liu J Q, Zeng T, Ye X M, Liu J P and Ma X F. 2021. Monitoring of the ice cracks and fracture process of the Brent ice shelf in Antarctica using HY-1C/D satellites. Haiyang Xuebao, 43(7): 205-206
刘建强, 曾韬, 叶小敏, 刘金普, 马小峰. 2021. HY-1C/D卫星对南极布伦特冰架冰裂缝变化与断裂过程的监测. 海洋学报, 43(7): 205-206 [DOI: 10.12284/hyxb2021155http://dx.doi.org/10.12284/hyxb2021155]
Liu R J, Xiao Y F, Ma Y, Cui T H and An J B. 2022. Red tide detection based on high spatial resolution broad band optical satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 184: 131-147 [DOI: 10.1016/j.isprsjprs.2021.12.009http://dx.doi.org/10.1016/j.isprsjprs.2021.12.009]
McClain C R. 2009. A decade of satellite ocean color observations. Annual Review of Marine Science, 1(1): 19-42 [DOI: 10.1146/annurev.marine.010908.163650http://dx.doi.org/10.1146/annurev.marine.010908.163650]
Mélin F and Franz B A. 2014. Assessment of satellite ocean colour radiometry and derived geophysical products// Zibordi G, Donlon C and Parr A eds. Optical Radiometry for Oceans Climate Measurements, chap. 6.1, Academic, Experimental Methods in the Physical Sciences 47: 609-638 [DOI: 10.1016/B978-0-12-417011-7.00020-9http://dx.doi.org/10.1016/B978-0-12-417011-7.00020-9]
Miao S S. 2018. A LM-2C successfully sends the HY-1C satellite into orbit. Aerospace China, (9): 26
苗珊珊. 2018. “长征”二号C运载火箭成功发射“海洋”一号C卫星. 中国航天, 9: 26 [DOI: 10.3969/j.issn.1002-7742.2018.09.008http://dx.doi.org/10.3969/j.issn.1002-7742.2018.09.008]
Morel A and Prieur L. 1977. Analysis of variations in ocean color. Limnology and Oceanography, 22(4): 709-722[DOI: 10.4319/lo.1977.22.4.0709http://dx.doi.org/10.4319/lo.1977.22.4.0709]
Mueller J L, Morel A, Frouin R, Davis C, Arnone R, Carder K, Lee Z P, Steward R G, Hooker S, Mobley C D, McLean S, Holben B, Miller M, Pietras C, Knobelspiesse K D, Fargion G S, Porter J and Voss K. 2003. Ocean Optics Protocols For Satellite Ocean Color Sensor Validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols. Greenbelt M D: Goddard Space Flight Space Centre: 1-84. (NASA/TM-2003-21621/Rev-Vol Ⅲ) [DOI: 10.25607/OBP-62http://dx.doi.org/10.25607/OBP-62]
Qin P, Simis S G H and Tilstone G H. 2017. Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET-OC. Remote Sensing of Environment, 200: 263-280 [DOI: 10.1016/j.rse.2017.08.024http://dx.doi.org/10.1016/j.rse.2017.08.024]
Ruddick K G, De Cauwer V, Park Y J and Moore G. 2006. Seaborne measurements of near infrared water‐leaving reflectance: the similarity spectrum for turbid waters. Limnology and Oceanography, 51(2): 1167-1179 [DOI 10.4319/lo.2006.51.2.1167http://dx.doi.org/10.4319/lo.2006.51.2.1167]
Shen Y F, Liu J Q, Ding J, Jiao J N, Sun S J and Lu Y C. 2020. HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. Journal of Remote Sensing, 24(8): 933-944
沈亚峰, 刘建强, 丁静, 焦俊男, 孙绍杰, 陆应诚. 2020. 海洋一号C星光学载荷对海面溢油的识别能力分析. 遥感学报, 24(8): 933-944 [DOI: 10.11834/jrs.20209475http://dx.doi.org/10.11834/jrs.20209475]
Suo Z Y, Lu Y C, Liu J Q, Ding J, Yin D Y, Xu F F and Jiao J N. 2021. Ultraviolet remote sensing of marine oil spills: a new approach of Haiyang-1C satellite. Optics Express, 29(9): 13486-13495 [DOI: 10.1364/OE.423702http://dx.doi.org/10.1364/OE.423702]
Teng Y, Zou B and Ye X M. 2022. Study on the chlorophyll ɑ concentration retrieved from HY-1C satellite coastal zone imager data. Haiyang Xuebao，44(5)：25-34
滕越, 邹斌, 叶小敏. 2022. HY-1C 卫星海岸带成像仪叶绿素ɑ反演研究. 海洋学报, 44(5): 1-10 [DOI: 10.12284/hyxb2022052http://dx.doi.org/10.12284/hyxb2022052]
Wang L M, Liu J, Gao J M, Yao B M, Yang F G and Zou J Q. 2019. Winter wheat early identification based on HY-1C/CZI data. Chinese Agricultural Science Bulletin, 35(33): 151-157
王利民, 刘佳, 高建孟, 姚保民, 杨福刚, 邹金秋. 2019. 基于HY-1C/CZI数据的冬小麦早期识别研究. 中国农学通报, 35(33): 151-157 [DOI: 10.11924/j.issn.1000-6850.casb20190600232http://dx.doi.org/10.11924/j.issn.1000-6850.casb20190600232]
Zhou Q, Liu J Q, Wang J R, Deng S Q and Tian L Q. 2020. Water turbidity monitoring of Zhiyin and Huangjia lakes in Wuhan for COVID-19 epidemic using HY-1C CZI data. Geomatics and Information Science of Wuhan University, 45(5): 676-681
周屈, 刘建强, 王剑茹, 邓实权, 田礼乔. 2020. 利用HY-1C卫星CZI数据在COVID-19疫情期间武汉知音湖和黄家湖的浊度监测研究. 武汉大学学报: 信息科学版, 45(5): 676-681 [DOI: 10.13203/j.whugis20200101http://dx.doi.org/10.13203/j.whugis20200101]
Zibordi G, Mélin F, Hooker S B, D'Alimonte D and Holben B. 2004. An autonomous above-water system for the validation of ocean color radiance data. IEEE Transactions on Geoscience and Remote Sensing, 42(2): 401-415 [DOI: 10.1109/TGRS.2003.821064http://dx.doi.org/10.1109/TGRS.2003.821064]
Zibordi G, Mélin F, Berthon J F, Holben B, Slutsker I, Giles D, D’Alimonte D, Vandemark D, Feng H, Schuster G, Fabbri B E, Kaitala S and Seppälä J. 2009a. AERONET-OC: a network for the validation of ocean color primary products. Journal of Atmospheric and Oceanic Technology, 26(8): 1634-1651 [DOI: 10.1175/2009JTECHO654.1http://dx.doi.org/10.1175/2009JTECHO654.1]
Zibordi G, Berthon J F, Mélin F, D'Alimonte D and Kaitala S. 2009b. Validation of satellite ocean color primary products at optically complex coastal sites: northern Adriatic Sea, Northern Baltic Proper and Gulf of Finland. Remote Sensing of Environment, 113(12): 2574-2591 [DOI: 10.1016/j.rse.2009.07.013http://dx.doi.org/10.1016/j.rse.2009.07.013]
Zou Y R, Liu J Q, Liang C and Zhu H T. 2020. Monitoring of mangrove growth using HY-1C Satellite CZI data based on remote sensing. Journal of Marine Sciences, 38(1): 68-76
邹亚荣, 刘建强, 梁超, 朱海天. 2020. 基于HY-1C卫星CZI数据的红树林长势遥感监测. 海洋学研究, 38(1): 68-76 [DOI: 10.3969/j.issn.1001-909X.2020.01.008http://dx.doi.org/10.3969/j.issn.1001-909X.2020.01.008]
相关文章
相关作者
相关机构
京公网安备11010802024621