Summary of sharing platforms for ocean color remote sensing in situ measurement data
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摘要: 高质量的原位测量数据是海洋水色遥感数据产品真实性检验、算法开发和气候变化研究的先决条件。原位数据的采集通常需要耗费大量的人力、物力、财力,单个研究团队采集的数据通常难以支持长时序和大范围的研究。在“大数据”科学研究的驱动下,国内外多个开放存取数据平台、政府间和国家级海洋科学数据中心以及主要涉海部门数据库平台发布了不同类型的海洋原位测量数据并向用户共享,以充分发挥原位测量数据的价值,支撑大科学问题的研究。由于各数据集在数据平台中离散分布,数据采集时间、区域、学科门类及数据获取方式不尽相同,数据使用者很难短时间内知晓并应用这些平台数据,导致搜集相关研究数据费时费力。因此,收集整理了29个可以获取海洋光学和海洋生物、地球、化学等参数的数据库平台,这些平台存储了全球海洋近百年来的原位测量数据,列举了共享数据在海洋水色遥感研究中的典型应用,并给出了常用参数的数据检索建议,以期帮助数据使用者快捷获取研究数据。Abstract: High-quality in situ measurement data is a prerequisite for the validation of ocean color remote sensing data products, algorithm development, and climate change research. The in situ measurement data were mainly collected through methods such as ship-based measurements, mooring platforms (buoys), and Argo profiling floats. However, these processes typically require a substantial investment of manpower, resources, and finances, and data collected by individual research teams often struggle to support long-term and large-scale studies. Driven by the advances in "big data" science, several open-access data platforms, intergovernmental and national marine science data centers, as well as database platforms of major marine-related departments, have released diverse types of marine in situ measurement data, making them accessible freely to users. It is difficult for data users to quickly understand and apply shared data from these platforms, because of the discrete distribution of datasets on different platforms, and differences in data collection time, regions, disciplinary categories, and acquisition methods. This results in a time-consuming and labor-intensive process of gathering relevant research data. 29 database platforms were compiled and organized, including the open-access data platforms, marine science data centers, and marine science long-time series observation stations, that can be used or have potential use value in ocean color remote sensing studies and provided examples of typical applications of the shared data within these platforms for various studies. The applications mainly include the alternative calibration and validation of satellite products, the development and improvement of remote sensing retrieval models for biogeochemical parameters, and research on the optical properties of seas. In terms of data sources, the shared data primarily originate from developed countries such as Europe and the United States. Temporally and spatially, the collection time of shared data spans a century, with the majority collected in the past 30 years and distributed mainly in the open oceans and coastal waters of countries such as the United States and Australia. Regarding data types, there are richness in ocean optical and biogeochemical parameters, but insufficient synchronous collection of both data, which may hamper the study of the optical characteristics of biogeochemical parameters.
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Key words:
- ocean color /
- ocean optics /
- bio-optics /
- in situ measurements /
- database /
- shared data /
- biogeochemistry
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表 1 《科学数据》发表数据及其部分应用案例
Tab. 1 Partial application cases of the data published in the Scientific Data
数据集描述 数据集 参数名称 应用案例 GLORIA - A globally representative hyperspectral in situ
dataset for optical sensing of water quality[16]文献[17] aCDOM, Chl a, Rrs, SDD, TSM 文献[12,18] A database of chlorophyll a in Australian waters[19] DOI:10.4225/69/586f220c3f708 Chl a 文献[20] Concentrations and ratios of particulate organic carbon,
nitrogen, and phosphorus in the global ocean[21]DOI: 10.5061/dryad.cnp5hqc17
https://www.bco-dmo.org/dataset/526747POC, PON, POP 文献[10,22] Nutrient, pigment, suspended matter and turbidity measurements
in the Belgian part of the North Sea[23]http://mda.vliz.be/ HPLC, TSM, 营养盐, 浑浊度 文献[24] 表 2 《地球系统科学数据》期刊发表数据的部分应用案例
Tab. 2 Partial application cases of the data published in the Earth System Science Data
数据集描述 数据集 参数名称 应用案例 The MAREDAT global database of high performance liquid
chromatography marine pigment measurements[25]文献[26] HPLC 文献[28–31] Coastcolour round robin data sets: a database to evaluate the performance of
algorithms for the retrieval of water quality parameters in coastal waters[32]文献[33] Chl a, TSM, CDOM 文献[12,34] Photosynthesis–irradiance parameters of marine phytoplankton:
synthesis of a global data set[35]文献[36] Chl a, 光合-辐照度参数 文献[37] A global compilation of in situ aquatic high spectral resolution inherent and
apparent optical property data for remote sensing applications[38]文献[39] atot, aCDOM, anap, ap, aph, bbtot, bbp, c, cp, R, Rrs 文献[24,40] Collection and analysis of a global marine phytoplankton
primary-production dataset[41]文献[42] Chl a, PP 文献[43] The HYPERMAQ dataset: bio-optical properties of moderately
to extremely turbid waters[44]文献[45] Chl a, HPLC, TSM, ρw, atot, c 文献[46] A compilation of global bio-optical in situ data for ocean colour
satellite applications – version three[47]文献[48] Chl a, aph, adg, bbp, Kd, Rrs, TSM 文献[12,49] 表 3 PANGAEA中部分数据应用案例
Tab. 3 Partial application cases of the data published in the PANGAEA
参数类别 参数名称 数据集 应用案例 生物、地球、
化学参数HPLC The MAREDAT global database of high performance liquid
chromatography marine pigment measurements[26];
Global surface ocean HPLC phytoplankton pigments and hyperspectral remote sensing reflectance[50];
Global retrieval of diatom abundance based on phytoplankton pigments and satellite data[51];
Phytoplankton pigment concentrations in the South Atlantic Ocean[52];
Phytoplankton pigment concentration and phytoplankton groups measured on water samples and from radiometric measurements obtained during POLARSTERN cruise PS113 in the Atlantic Ocean[53]文献[11,29,40,54] TSM Suspended particulate matter concentrations and organic matter fractions from water samples[55] 文献[56] DOC Hydrographical, biogeochemical and biooptical water properties in the Mackenzie
Delta Region during 4 expeditions from spring to fall in 2019[57]文献[58–59] Chla GLORIA - A global dataset of remote sensing reflectance and
water quality from inland and coastal waters[17];
The HYPERMAQ dataset[45];
A compilation of global bio-optical in situ data for ocean-colour satellite applications - version 3[48]文献[46] POC Spring phytoplankton communities of the Labrador Sea (2005−2014): pigment signatures,
photophysiology and elemental ratios[60]— PP Global marine phytoplankton production dataset[42] 文献[61] PFT-Chl a Global data sets of Chlorophyll a concentration for diatoms, coccolithophores
(haptophytes) and cyanobacteria obtained from in situ observations and satellite retrievals[62][63] 表观光学量(AOPs) Rrs GLORIA - A global dataset of remote sensing reflectance and water quality from inland and coastal waters[17];
CoastColour Round Robin datasets, Version 1[33];
In situ high spectral resolution inherent and apparent optical
property data from diverse aquatic environments[39];
A compilation of global bio-optical in situ data for ocean-colour satellite applications - version 3[48];
Global surface ocean HPLC phytoplankton pigments and
hyperspectral remote sensing reflectance[50];
The SeaSWIR dataset[64];
Remote sensing reflectance during POLARSTERN cruise ANT-XXV/1[65];文献[12,18,32] 固有光学量(IOPs) ap Properties of seawater and particulate matter from a WETLabs AC-S spectrophotometer
and a WETLabs chlorophyll fluorometer mounted on the continuous surface water
sampling system during the Tara Oceans expedition 2009−2013[66]文献[67] aph GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality[16] — bbp GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality[68] — 表 4 Dryad中部分数据应用案例
Tab. 4 Partial application cases of the data published in the Dryad
数据集 参数名称 应用案例 Bio-optical Database of the Arctic Ocean [DOI: 10.5061/dryad.cnp5hqc17] Rrs, Kd, atot, aph, bbtot, bbp, POC, Chl a 文献[27] Data from: Concentrations and ratios of particulate organic carbon, nitrogen,
and phosphorus in the global ocean [DOI: 10.5061/dryad.d702p]POC, PON, POP 文献[21] A synthetic database of hyperspectral ocean optical properties [DOI: 10.6076/D1630T] Rrs 文献[69] Data from: Effects of sea ice cover on satellite-detected primary
production in the Arctic Ocean [DOI: 10.5061/dryad.34f4q]PP 文献[70] Tracking freshwater browning and coastal water darkening from boreal forests
to the Arctic Ocean [DOI: 10.5061/dryad.xwdbrv1gq]Chl a, SDD 文献[71] 表 5 Zenodo中部分数据应用案例
Tab. 5 Partial application cases of the data published in the Zenodo
数据集 参数名称 应用案例 Baltic Sea shipborne Hyperspectral Reflectance data from 2016.
[DOI: 10.5281/zenodo.5572537]Rrs 文献[12,72] Data set for the paper: Intercomparison of ocean colour algorithms for picophytoplankton
carbon in the ocean. [DOI: 10.5281/zenodo.1067229]Pico-Cphy 文献[31] Particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition of seawater sampled during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017. [DOI: 10.5281/zenodo.3859515] POC, PON 文献[73] Phytoplankton pigment concentrations of seawater sampled during the Antarctic Circumnavigation Expedition (ACE)
during the Austral Summer of 2016/2017. [DOI: 10.5281/zenodo.3816726]HPLC 文献[74] Particulate light absorption coefficients (350 – 750 nm) measured using the filter pad method during the Antarctic
Circumnavigation Expedition (ACE) during the austral summer of 2016/2017. [DOI: 10.5281/zenodo.3993096]ap 文献[74] Sky irradiance over photosynthetically active radiation wavelengths (400−700 nm) recorded shipboard during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017. [DOI: 10.5281/zenodo.3859836] PAR [74] 表 6 数据共享平台综合对比
Tab. 6 Comprehensive comparison of data-sharing platforms
章节 数据共享平台 是否开放 时间跨度 空间覆盖 数据类型* 易用性** 3.1 国际海洋数据和信息交换中心 (IODE) 是 1884− 全球 ABCDEF 良 3.2 世界海洋数据库 (WOD) 是 1772− 全球 ABCDEF 良 3.3 海洋数据网 (SeaDataNet) 是 1805− 全球 ABCDEF 良 3.4 国际海洋考察理事会 (ICES) 是 1877− 大西洋、北太平洋、北极、地中海、黑海等 ABC 优 3.5 SeaWiFS生物光学档案和存储系统 (SeaBASS) 是 1933− 全球 ABCDF 优 3.6 生物和化学海洋学数据管理办公室 (BCO-DMO) 是 1949− 全球 ABCDEF 良 3.7 英国海洋数据中心 (BODC) 是 1842− 全球 ABCDEF 良 3.8 中国国家海洋科学数据中心 是 1846− 全球 ABCDEF 优 3.9 澳大利亚海洋数据网 (AODN) 是 1844− 澳大利亚沿岸 ABCDF 优 3.10 日本海洋数据中心 (JODC) 是 1772− 日本近海 ABC 优 3.11 加利福尼亚海洋渔业合作调查 (CalCOFI) 是 1949− 美国近海 ABC 优 3.12 近岸和海洋观察 (CoastWatch • OceanWatch) 是 2014−2021 美国近海 CD 优 3.13 中国南海海洋数据中心 申请 1959− 中国南海 ABCDEF 差 3.14 香港环境保护署环境保护互动中心 是 1986− 中国南海 ABC 优 3.15 持续浮游生物记录(CPR)调查 是 1931− 北大西洋 C 差 3.16 帕尔默长期生态研究 (Palmer LTER) 是 1989− 南极 ACD 良 4.1 海洋光学浮标 (MOBY) 是 1997− 北太平洋 D 良 4.2 长时间序列光学浮标 (BOUSSOLE) 是 2003−2023 地中海 ACD 优 4.3 气溶胶自动网络 (AERONET) 是 1992− 全球 DF 优 4.4 生物地球化学剖面浮标 (BGC-Argo) 是 2002− 全球 ABCD 优 4.5 夏威夷海洋时间序列 (HOT) 是 1988− 北太平洋 ABCD 优 百慕大大西洋时间序列 (BATS) 是 1988− 北大西洋 ABCD 优 4.6 欧洲加那利群岛海洋时间序列站 (ESTOC) 是 1994− 北大西洋 ABC 优 4.7 黄东海光学遥感海上检验场 否 2019− 黄/东海 DF − * A:海洋物理,B:海洋化学,C:海洋生物,D:海洋光学,E:海洋地质,F:大气科学。** 通过下载Chl a或Lwn原位测量数据所需的步骤数评估各数据平台的易用性。 A1 名词缩写说明表
A1 Glossary of abbreviations
缩写 英文全称 中文全称 aCDOM Absorption coefficient by the colored dissolved organic matter 有色溶解有机物的吸收系数 AOD Aerosol optical depth 气溶胶光学深度 AOP Apparent optical property 表观光学属性 adg Detrital plus CDOM absorption coefficient 碎屑与有色溶解有机物吸收系数之和 anap Absorption coefficient by the non-algal particles 非藻类颗粒物吸收系数 ap Absorption coefficient by the particles 颗粒物吸收系数 aph Absorption coefficient by the phytoplankton 浮游植物吸收系数 atot Total absorption coefficient 总吸收系数 bbp Particulate backscattering coefficients 颗粒物后向散射系数 bbtot Total backscattering coefficients 总后向散射系数 ctot Beam attenuation coefficient 光束衰减系数 cp Particulate attenuation coefficient 颗粒物衰减系数 CDOM Colored dissolved organic matter 有色溶解有机物 Chla Chlorophyll-a 叶绿素-a DOC Dissolved Organic Carbon 溶解有机碳 Ed Downward irradiance 下行辐照度 HPLC High Performance Liquid Chromatography 高效液相色谱 Kd Diffuse attenuation coefficient 漫射衰减系数 IOP Inherent optical properties 固有光学属性 Lw Water-leaving radiance 离水辐亮度 Lwn Normalized water-leaving radiance 归一化离水辐亮度 PAR Photosynthetically active radiation 光和有效辐射 PFT Phytoplankton functional type 浮游植物功能类型 Pico-Cphy Pico- Phytoplankton carbon Pico级浮游植物碳 POC Particulate organic carbon 颗粒有机碳 PON Particulate organic nitrogen 颗粒有机氮 POP Particulate organic phosphorus 颗粒有机磷 PP Primary production 初级生产力 PSD Particle size distribution 颗粒物粒径分布 R Irradiance reflectance 辐照度反射率 Rrs Remote sensing reflectance 遥感反射比 SDD Secchi disk depth 透明度 TSM Total suspended matter 总悬浮物 TSS Total suspended solids 总悬浮固体 ρw Water-leaving reflectance 离水反射率 A2 数据共享平台网址汇总
A2 Summary of data-sharing platform websites
章节 平台名称 网址[2023年11月] 2.1 《科学数据》(Scientific Data) https://www.nature.com/sdata/ 2.2 《地球系统科学数据》(Earth System Science Data) https://www.earth-system-science-data.net/ 2.3 地球与环境科学数据出版社—PANGAEA https://www.pangaea.de/ 2.4 开放数据发布平台—Dryad https://datadryad.org/ 2.5 数字图书馆—Zenodo https://zenodo.org/ 3.1 国际海洋数据和信息交换中心 (IODE) https://www.iode.org/ 3.2 世界海洋数据库 (WOD) https://www.ncei.noaa.gov/products/world-ocean-database/ 3.3 海洋数据网 (SeaDataNet) https://www.seadatanet.org/ 3.4 国际海洋考察理事会 (ICES) https://www.ices.dk/ 3.5 SeaWiFS生物光学档案和存储系统 (SeaBASS) https://seabass.gsfc.nasa.gov/ 3.6 生物和化学海洋学数据管理办公室 (BCO-DMO) http://bco-dmo.org/ 3.7 英国海洋数据中心 (BODC) https://www.bodc.ac.uk/ 3.8 中国国家海洋科学数据中心 https://mds.nmdis.org.cn/ 3.9 澳大利亚海洋数据网 (AODN) http://portal.aodn.org.au/ 3.10 日本海洋数据中心 (JODC) https://www.jodc.go.jp/ 3.11 加利福尼亚海洋渔业合作调查 (CalCOFI) https://calcofi.org/ 3.12 近岸和海洋观察 (CoastWatch • OceanWatch) https://coastwatch.noaa.gov/insitu/insituSearch.html 3.13 中国南海海洋数据中心 http://data.scsio.ac.cn/ 3.14 香港环境保护署环境保护互动中心 https://cd.epic.epd.gov.hk/EPICDI/ 3.15 持续浮游生物记录(CPR)调查 https://www.cprsurvey.org/ 3.16 帕尔默长期生态研究 (Palmer LTER) https://pallter.marine.rutgers.edu/ 4.1 海洋光学浮标 (MOBY) https://mlml.sjsu.edu/moby/ 4.2 长时间序列光学浮标 (BOUSSOLE) http://www.obs-vlfr.fr/Boussole/ 4.3 气溶胶自动网络 (AERONET) https://aeronet.gsfc.nasa.gov/ 4.4 生物地球化学剖面浮标 (BGC-Argo) https://biogeochemical-argo.org/ 4.5 夏威夷海洋时间序列 (HOT) https://hahana.soest.hawaii.edu/hot/ 百慕大大西洋时间序列 (BATS) https://bats.bios.asu.edu/ 4.6 欧洲加那利群岛海洋时间序列站 (ESTOC) https://plocan.eu/ -
[1] Gordon H R, Morel A Y. Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A review[M]. New York: Springer-Verlag, 1983: 292. [2] Platt T, Hoepffner N, Stuart V, et al. Why ocean colour? The societal benefits of ocean-colour technology[R]. Dartmouth: International Ocean-Colour Coordinating Group (IOCCG), 2008: 141. [3] 潘德炉, 白雁. 我国海洋水色遥感应用工程技术的新进展[J]. 中国工程科学, 2008, 10(9): 14−24,46.Pan Delu, Bai Yan. Advances on the application of ocean color remote sensing engineering in China[J]. Strategic Study of Chinese Academy of Engineerng, 2008, 10(9): 14−24,46. [4] 白雁, 崔廷伟, 冯烁, 等. 水色学概览[M]. 厦门: 厦门大学出版社, 2019.Bai Yan, Cui Tingwei, Feng Shuo, et al. Overlook of Ocean Color[M]. Xiamen: Xiamen University Press, 2019. (查阅网上资料, 未找到本条文献英文信息, 请确认) [5] Hooker S B, Mcclain C R. The calibration and validation of SeaWiFS data[J]. Progress in Oceanography, 2000, 45(3/4): 427−465. [6] Mueller J L, Morel A, Frouin R, et al. Ocean optics protocols for satellite ocean color sensor validation, Revision 4. Volume III: radiometric measurements and data analysis protocols[R]. Greenbelt, MD: Goddard Space Flight Space Center, 2003: 1−63. [7] Zibordi G, Voss K J, Johnson B C, et al. Ocean optics and biogeochemistry protocols for satellite ocean colour sensor validation, Volume 3.0: protocols for satellite ocean colour data validation: in situ optical radiometry[R]. Dartmouth: IOCCG, 2019. [8] Fargion G S, McClain C R, Werdell P J, et al. The SeaWiFS bio-optical archive and storage system (SeaBASS): current architecture and implementation[R]. Greenbelt, Maryland: Goddard Space Flight Center, 2002. [9] Brewin R J W, Sathyendranath S, Kulk G, et al. Ocean carbon from space: current status and priorities for the next decade[J]. Earth-Science Reviews, 2023, 240: 104386. doi: 10.1016/j.earscirev.2023.104386 [10] Li Qiang, Jiang Lingling, Chen Yanlong, et al. Absorption-based algorithm for satellite estimating the particulate organic carbon concentration in the global surface ocean[J]. Frontiers in Marine Science, 2023, 9: 1048893. doi: 10.3389/fmars.2022.1048893 [11] Zhang Yuan, Shen Fang, Sun Xuerong, et al. Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020)[J]. Remote Sensing of Environment, 2023, 294: 113596. doi: 10.1016/j.rse.2023.113596 [12] Men Jilin, Chen Xi, Hou Xuejiao, et al. OC_3S: an optical classification and spectral scoring system for global waters using UV–visible remote sensing reflectance[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200: 153−172. doi: 10.1016/j.isprsjprs.2023.05.017 [13] Bonelli A G, Loisel H, Jorge D S F, et al. A new method to estimate the dissolved organic carbon concentration from remote sensing in the global open ocean[J]. Remote Sensing of Environment, 2022, 281: 113227. doi: 10.1016/j.rse.2022.113227 [14] Wei Jianwei, Wang Menghua, Jiang Lide, et al. Global estimation of suspended particulate matter from satellite ocean color imagery[J]. Journal of Geophysical Research: Oceans, 2021, 126(8): e2021JC017303. doi: 10.1029/2021JC017303 [15] Krishna K V, Shanmugam P, Sarangi R K. Robust algorithm based on the reflectance curvature for estimating particulate organic carbon and its spatiotemporal variability in the global ocean[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4207116. [16] Lehmann M K, Gurlin D, Pahlevan N, et al. GLORIA - a globally representative hyperspectral in situ dataset for optical sensing of water quality[J]. Scientific Data, 2023, 10(1): 100. doi: 10.1038/s41597-023-01973-y [17] Lehmann M K, Gurlin D, Pahlevan N, et al. GLORIA - a global dataset of remote sensing reflectance and water quality from inland and coastal waters[DS]. PANGAEA, 2022. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [18] Burket M O, Olmanson L G, Brezonik P L. Comparison of two water color algorithms: implications for the remote sensing of water bodies with moderate to high CDOM or chlorophyll levels[J]. Sensors, 2023, 23(3): 1071. doi: 10.3390/s23031071 [19] Davies C H, Ajani P, Armbrecht L, et al. A database of chlorophyll a in Australian waters[J]. Scientific Data, 2018, 5(1): 180018. doi: 10.1038/sdata.2018.18 [20] Baldry K, Strutton P G, Hill N A, et al. Subsurface chlorophyll-a maxima in the Southern Ocean[J]. Frontiers in Marine Science, 2020, 7: 671. doi: 10.3389/fmars.2020.00671 [21] Martiny A C, Vrugt J A, Lomas M W. Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean[J]. Scientific Data, 2014, 1(1): 140048. doi: 10.1038/sdata.2014.48 [22] Evers-King H, Martinez-Vicente V, Brewin R J W, et al. Validation and intercomparison of ocean color algorithms for estimating particulate organic carbon in the oceans[J]. Frontiers in Marine Science, 2017, 4: 251. doi: 10.3389/fmars.2017.00251 [23] Mortelmans J, Deneudt K, Cattrijsse A, et al. Nutrient, pigment, suspended matter and turbidity measurements in the Belgian part of the North Sea[J]. Scientific Data, 2019, 6(1): 22. doi: 10.1038/s41597-019-0032-7 [24] Dierssen H M, Ackleson S G, Joyce K E, et al. Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook[J]. Frontiers in Environmental Science, 2021, 9: 649528. doi: 10.3389/fenvs.2021.649528 [25] Peloquin J, Swan C, Gruber N, et al. The MAREDAT global database of high performance liquid chromatography marine pigment measurements[J]. Earth System Science Data, 2013, 5(1): 109−123. doi: 10.5194/essd-5-109-2013 [26] Peloquin J M, Swan C, Gruber N, et al. The MAREDAT global database of high performance liquid chromatography marine pigment measurements - gridded data product (NetCDF) - contribution to the MAREDAT world ocean atlas of plankton functional types[DS]. PANGAEA, 2013. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [27] Lewis K M, Van Dijken G L, Arrigo K R. Changes in phytoplankton concentration now drive increased Arctic Ocean primary production[J]. Science, 2020, 369(6500): 198−202. doi: 10.1126/science.aay8380 [28] Puissant A, El Hourany R, Charantonis A A, et al. Inversion of phytoplankton pigment vertical profiles from satellite data using machine learning[J]. Remote Sensing, 2021, 13(8): 1445. doi: 10.3390/rs13081445 [29] Xi Hongyan, Losa S N, Mangin A, et al. Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data[J]. Remote Sensing of Environment, 2020, 240: 111704. doi: 10.1016/j.rse.2020.111704 [30] El Hourany R, Saab M A A, Faour G, et al. Estimation of secondary phytoplankton pigments from satellite observations using self-organizing maps (SOMs)[J]. Journal of Geophysical Research: Oceans, 2019, 124(2): 1357−1378. doi: 10.1029/2018JC014450 [31] Martínez-Vicente V, Evers-King H, Roy S, et al. Intercomparison of ocean color algorithms for picophytoplankton carbon in the ocean[J]. Frontiers in Marine Science, 2017, 4: 378. doi: 10.3389/fmars.2017.00378 [32] Nechad B, Ruddick K, Schroeder T, et al. CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters[J]. Earth System Science Data, 2015, 7(2): 319−348. doi: 10.5194/essd-7-319-2015 [33] Nechad B, Ruddick K, Schroeder T, et al. CoastColour Round Robin datasets, Version 1[DS]. PANGAEA, 2015. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [34] Lavigne H, Van Der Zande D, Ruddick K, et al. Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters[J]. Remote Sensing of Environment, 2021, 255: 112237. doi: 10.1016/j.rse.2020.112237 [35] Bouman H A, Platt T, Doblin M, et al. Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set[J]. Earth System Science Data, 2018, 10(1): 251−266. doi: 10.5194/essd-10-251-2018 [36] Bouman H A, Platt T, Doblin M A, et al. A global dataset of photosynthesis-irradiance parameters for marine phytoplankton[DS]. PANGAEA, 2017. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [37] Kulk G, Platt T, Dingle J, et al. Primary production, an index of climate change in the ocean: satellite-based estimates over two decades[J]. Remote Sensing, 2020, 12(5): 826. doi: 10.3390/rs12050826 [38] Casey K A, Rousseaux C S, Gregg W W, et al. A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications[J]. Earth System Science Data, 2020, 12(2): 1123−1139. doi: 10.5194/essd-12-1123-2020 [39] Casey K A, Rousseaux C S, Gregg W W, et al. In situ high spectral resolution inherent and apparent optical property data from diverse aquatic environments[DS]. PANGAEA, 2019. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [40] Kramer S J, Siegel D A, Maritorena S, et al. Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales[J]. Remote Sensing of Environment, 2022, 270: 112879. doi: 10.1016/j.rse.2021.112879 [41] Mattei F, Scardi M. Collection and analysis of a global marine phytoplankton primary-production dataset[J]. Earth System Science Data, 2021, 13(10): 4967−4985. doi: 10.5194/essd-13-4967-2021 [42] Mattei F, Scardi M. Global marine phytoplankton production dataset[DS]. PANGAEA, 2021. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [43] Xu Lei, Yu Hongchu, Chen Zeqiang, et al. Monthly ocean primary productivity forecasting by joint use of seasonal climate prediction and temporal memory[J]. Remote Sensing, 2023, 15(5): 1417. doi: 10.3390/rs15051417 [44] Lavigne H, Dogliotti A, Doxaran D, et al. The HYPERMAQ dataset: bio-optical properties of moderately to extremely turbid waters[J]. Earth System Science Data, 2022, 14(11): 4935−4947. doi: 10.5194/essd-14-4935-2022 [45] Lavigne H, Dogliotti A I, Doxaran D, et al. The HYPERMAQ dataset[DS]. PANGAEA, 2022. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [46] Bi Shun, Hieronymi M, Röttgers R. Bio-geo-optical modelling of natural waters[J]. Frontiers in Marine Science, 2023, 10: 1196352. doi: 10.3389/fmars.2023.1196352 [47] Valente A, Sathyendranath S, Brotas V, et al. A compilation of global bio-optical in situ data for ocean colour satellite applications – version three[J]. Earth System Science Data, 2022, 14(12): 5737−5770. doi: 10.5194/essd-14-5737-2022 [48] Valente A, Sathyendranath S, Brotas V, et al. A compilation of global bio-optical in situ data for ocean-colour satellite applications - version 3[DS]. PANGAEA, 2022. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [49] Hieronymi M, Bi Shun, Müller D, et al. Ocean color atmospheric correction methods in view of usability for different optical water types[J]. Frontiers in Marine Science, 2023, 10: 1129876. doi: 10.3389/fmars.2023.1129876 [50] Kramer S J, Siegel D A, Maritorena S, et al. Global surface ocean HPLC phytoplankton pigments and hyperspectral remote sensing reflectance[DS]. PANGAEA, 2021. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [51] Soppa M A, Hirata T, Silva B, et al. Global retrieval of diatom abundance based on phytoplankton pigments and satellite data[J]. Remote Sensing, 2014, 6(10): 10089−10106. doi: 10.3390/rs61010089 [52] Soppa M A, Hirata T, Silva B, et al. Phytoplankton pigment concentrations in the South Atlantic Ocean[DS]. PANGAEA, 2014. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [53] Bracher A, Xi Hongyan, Dinter T, et al. Phytoplankton pigment concentration and phytoplankton groups measured on water samples and from radiometric measurements obtained during POLARSTERN cruise PS113 in the Atlantic Ocean[DS]. PANGAEA, 2020. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [54] Bracher A, Xi Hongyan, Dinter T, et al. High resolution water column phytoplankton composition across the atlantic ocean from ship-towed vertical undulating radiometry[J]. Frontiers in Marine Science, 2020, 7: 235. doi: 10.3389/fmars.2020.00235 [55] Riethmüller R, Flöser G. Suspended particulate matter concentrations and organic matter fractions from water samples[DS]. PANGAEA, 2017. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [56] Schartau M, Riethmüller R, Flöser G, et al. On the separation between inorganic and organic fractions of suspended matter in a marine coastal environment[J]. Progress in Oceanography, 2019, 171: 231−250. doi: 10.1016/j.pocean.2018.12.011 [57] Juhls B, Lizotte M, Matsuoka A, et al. Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019[DS]. PANGAEA, 2021. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [58] Juhls B, Matsuoka A, Lizotte M, et al. Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters: implications for ocean colour remote sensing[J]. Remote Sensing of Environment, 2022, 283: 113327. doi: 10.1016/j.rse.2022.113327 [59] Gonçalves-Araujo R, Granskog M A, Osburn C L, et al. A Pan-Arctic algorithm to estimate dissolved organic carbon concentrations from colored dissolved organic matter spectral absorption[J]. Geophysical Research Letters, 2023, 50(21): e2023GL105028. doi: 10.1029/2023GL105028 [60] Fragoso G M, Poulton A J, Yashayaev I M, et al. Spring phytoplankton communities of the Labrador Sea (2005–2014): pigment signatures, photophysiology and elemental ratios[J]. Biogeosciences, 2017, 14(5): 1235−1259. doi: 10.5194/bg-14-1235-2017 [61] Ping Bo, Meng Yunshan, Xue Cunjin, et al. Oceanic primary production estimation based on machine learning[J]. Journal of Geophysical Research: Oceans, 2023, 128(5): e2022JC018980. doi: 10.1029/2022JC018980 [62] Losa S N, Soppa M A, Dinter T, et al. Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT)[J]. Frontiers in Marine Science, 2017, 4: 203. doi: 10.3389/fmars.2017.00203 [63] Pradhan H K, Völker C, Losa S N, et al. Assimilation of global total chlorophyll OC-CCI data and its impact on individual phytoplankton fields[J]. Journal of Geophysical Research: Oceans, 2019, 124(1): 470−490. doi: 10.1029/2018JC014329 [64] Knaeps E, Doxaran D, Dogliotti A I, et al. The SeaSWIR dataset[DS]. PANGAEA, 2018. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [65] Taylor B B, Torrecilla E, Bernhardt A, et al. Remote sensing reflectance during POLARSTERN cruise ANT-XXV/1[DS]. PANGAEA, 2011. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [66] Boss E, Picheral M, Slade W, et al. [RAW VALIDATED DATA] Properties of seawater and particulate matter from a WETLabs AC-S spectrophotometer and a WETLabs chlorophyll fluorometer mounted on the continuous surface water sampling system during the Tara Oceans expedition 2009−2013[DS]. PANGAEA, 2014. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [67] Boss E, Picheral M, Leeuw T, et al. The characteristics of particulate absorption, scattering and attenuation coefficients in the surface ocean; contribution of the Tara Oceans expedition[J]. Methods in Oceanography, 2013, 7: 52−62. doi: 10.1016/j.mio.2013.11.002 [68] Valente A, Sathyendranath S, Brotas V, et al. A compilation of global bio-optical in situ data for ocean-colour satellite applications - version 3[DS]. PANGAEA, 2022. (查阅网上资料, 未找到出版地信息, 请确认补充, 且不确定文献类型是否正确, 请确认) [69] Loisel H, Jorge D S F, Reynolds R A, et al. A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean[J]. Earth System Science Data, 2023, 15(8): 3711−3731. doi: 10.5194/essd-15-3711-2023 [70] Kahru M, Lee Z, Mitchell B G, et al. Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean[J]. Biology Letters, 2016, 12(11): 20160223. doi: 10.1098/rsbl.2016.0223 [71] Opdal A F, Andersen T, Hessen D O, et al. Tracking freshwater browning and coastal water darkening from boreal forests to the Arctic Ocean[J]. Limnology and Oceanography Letters, 2023, 8(4): 611−619. doi: 10.1002/lol2.10320 [72] Qin Ping, Simis S G H, Tilstone G H. Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET-OC[J]. Remote Sensing of Environment, 2017, 200: 263−280. doi: 10.1016/j.rse.2017.08.024 [73] Moallemi A, Landwehr S, Robinson C, et al. Sources, occurrence and characteristics of fluorescent biological aerosol particles measured over the pristine Southern Ocean[J]. Journal of Geophysical Research: Atmospheres, 2021, 126(11): e2021JD034811. doi: 10.1029/2021JD034811 [74] Robinson C M, Huot Y, Schuback N, et al. High latitude Southern Ocean phytoplankton have distinctive bio-optical properties[J]. Optics Express, 2021, 29(14): 21084−21112. doi: 10.1364/OE.426737 [75] Pitarch J, Volpe G, Colella S, et al. Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data[J]. Ocean Science, 2016, 12(2): 379−389. doi: 10.5194/os-12-379-2016 [76] Ciavatta S, Brewin R J W, Skákala J, et al. Assimilation of ocean-color plankton functional types to improve marine ecosystem simulations[J]. Journal of Geophysical Research: Oceans, 2018, 123(2): 834−854. doi: 10.1002/2017JC013490 [77] Werdell P J, Bailey S W. An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation[J]. Remote Sensing of Environment, 2005, 98(1): 122−140. doi: 10.1016/j.rse.2005.07.001 [78] He Xianqiang, Bai Yan, Pan Delu, et al. Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters[J]. Optics Express, 2012, 20(18): 20754−20770. doi: 10.1364/OE.20.020754 [79] Wang Menghua, Son S, Shi Wei. Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data[J]. Remote Sensing of Environment, 2009, 113(3): 635−644. doi: 10.1016/j.rse.2008.11.005 [80] Wang Menghua, Son S, Harding Jr L W. Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications[J]. Journal of Geophysical Research: Oceans, 2009, 114(C10): C10011. [81] Moore T S, Campbell J W, Hui Feng. A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(8): 1764−1776. doi: 10.1109/36.942555 [82] Hu Chuanmin, Lee Z, Franz B. Chlorophyll aalgorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference[J]. Journal of Geophysical Research: Oceans, 2012, 117(C1): C01011. [83] Johnson R, Strutton P G, Wright S W, et al. Three improved satellite chlorophyll algorithms for the Southern Ocean[J]. Journal of Geophysical Research: Oceans, 2013, 118(7): 3694−3703. doi: 10.1002/jgrc.20270 [84] 崔廷伟, 张杰, 马毅, 等. 北冰洋卫星水色遥感观测能力评价与展望[J]. 中国海洋大学学报(自然科学版), 2021, 51(1): 125−137.Cui Tingwei, Zhang Jie, Ma Yi, et al. Overview and Prospect of satellite ocean colour observation over the Arctic Ocean[J]. Periodical of Ocean University of China, 2021, 51(1): 125−137. [85] Kostadinov T S, Milutinović S, Marinov I, et al. Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution[J]. Ocean Science, 2016, 12(2): 561−575. doi: 10.5194/os-12-561-2016 [86] Liu Huizeng, Li Qingquan, Bai Yan, et al. Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods[J]. Remote Sensing of Environment, 2021, 256: 112316. doi: 10.1016/j.rse.2021.112316 [87] Le Chengfeng, Zhou Xueying, Hu Chuanmin, et al. A color-index-based empirical algorithm for determining particulate organic carbon concentration in the ocean from satellite observations[J]. Journal of Geophysical Research: Oceans, 2018, 123(10): 7407−7419. doi: 10.1029/2018JC014014 [88] Le Chengfeng, Wu Ming, Cai Sunbin, et al. Remote sensing of particulate organic carbon on the Western Antarctic Peninsula shelf using a color index-based algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4203113. [89] Loisel H, Bosc E, Stramski D, et al. Seasonal variability of the backscattering coefficient in the Mediterranean Sea based on satellite SeaWiFS imagery[J]. Geophysical Research Letters, 2001, 28(22): 4203−4206. doi: 10.1029/2001GL013863 [90] Cetinić I, Perry M J, Briggs N T, et al. Particulate organic carbon and inherent optical properties during 2008 North Atlantic Bloom Experiment[J]. Journal of Geophysical Research: Oceans, 2012, 117(C6): C06028. [91] Cetinić I, Perry M J, D’asaro E, et al. A simple optical index shows spatial and temporal heterogeneity in phytoplankton community composition during the 2008 North Atlantic Bloom Experiment[J]. Biogeosciences, 2015, 12(7): 2179−2194. doi: 10.5194/bg-12-2179-2015 [92] Joshi I D, Stramski D, Reynolds R A, et al. Performance assessment and validation of ocean color sensor-specific algorithms for estimating the concentration of particulate organic carbon in oceanic surface waters from satellite observations[J]. Remote Sensing of Environment, 2023, 286: 113417. doi: 10.1016/j.rse.2022.113417 [93] Tehrani N C, D’Sa E J, Osburn C L, et al. Chromophoric dissolved organic matter and dissolved organic carbon from sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS sensors: case study for the northern Gulf of Mexico[J]. Remote Sensing, 2013, 5(3): 1439−1464. doi: 10.3390/rs5031439 [94] Arteaga L A, Behrenfeld M J, Boss E, et al. Vertical structure in phytoplankton growth and productivity inferred from Biogeochemical-Argo floats and the Carbon-based productivity model[J]. Global Biogeochemical Cycles, 2022, 36(8): e2022GB007389. doi: 10.1029/2022GB007389 [95] Park J, Kim H C, Bae D, et al. Data reconstruction for remotely sensed chlorophyll-a concentration in the Ross Sea using ensemble-based machine learning[J]. Remote Sensing, 2020, 12(11): 1898. doi: 10.3390/rs12111898 [96] Świrgoń M, Stramska M. Comparison of in situ and satellite ocean color determinations of particulate organic carbon concentration in the global ocean[J]. Oceanologia, 2015, 57(1): 25−31. doi: 10.1016/j.oceano.2014.09.002 [97] Rasse R, Dall’olmo G, Graff J, et al. Evaluating optical proxies of particulate organic carbon across the Surface Atlantic Ocean[J]. Frontiers in Marine Science, 2017, 4: 367. doi: 10.3389/fmars.2017.00367 [98] Koestner D, Stramski D, Reynolds R A. A multivariable empirical algorithm for estimating particulate organic carbon concentration in marine environments from optical backscattering and chlorophyll-a measurements[J]. Frontiers in Marine Science, 2022, 9: 941950. doi: 10.3389/fmars.2022.941950 [99] Graff J R, Westberry T K, Milligan A J, et al. Analytical phytoplankton carbon measurements spanning diverse ecosystems[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2015, 102: 16−25. doi: 10.1016/j.dsr.2015.04.006 [100] Mitchell C, Hu C, Bowler B, et al. Estimating particulate inorganic carbon concentrations of the global ocean from ocean color measurements using a reflectance difference approach[J]. Journal of Geophysical Research: Oceans, 2017, 122(11): 8707−8720. doi: 10.1002/2017JC013146 [101] Organelli E, Dall’Olmo G, Brewin R J W, et al. The open-ocean missing backscattering is in the structural complexity of particles[J]. Nature Communications, 2018, 9(1): 5439. doi: 10.1038/s41467-018-07814-6 [102] Kostadinov T S, Robertson Lain L, Kong C E, et al. Ocean color algorithm for the retrieval of the particle size distribution and carbon-based phytoplankton size classes using a two-component coated-sphere backscattering model[J]. Ocean Science, 2023, 19(3): 703−727. doi: 10.5194/os-19-703-2023 [103] Brewin R J W, Dall’Olmo G, Pardo S, et al. Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals[J]. Remote Sensing of Environment, 2016, 183: 82−97. doi: 10.1016/j.rse.2016.05.005 [104] Gregg W W, Casey N W. Improving the consistency of ocean color data: a step toward climate data records[J]. Geophysical Research Letters, 2010, 37(4): L04605. [105] Sun Xuerong, Brewin R J W, Sathyendranath S, et al. Coupling ecological concepts with an ocean-colour model: phytoplankton size structure[J]. Remote Sensing of Environment, 2023, 285: 113415. doi: 10.1016/j.rse.2022.113415 [106] Schroeder T, Schaale M, Lovell J, et al. An ensemble neural network atmospheric correction for Sentinel-3 OLCI over coastal waters providing inherent model uncertainty estimation and sensor noise propagation[J]. Remote Sensing of Environment, 2022, 270: 112848. doi: 10.1016/j.rse.2021.112848 [107] Siswanto E, Ishizaka J, Yokouchi K. Optimal primary production model and parameterization in the eastern East China Sea[J]. Journal of Oceanography, 2006, 62(3): 361−372. doi: 10.1007/s10872-006-0061-7 [108] Kahru M, Kudela R M, Anderson C R, et al. Evaluation of satellite retrievals of ocean chlorophyll-a in the California Current[J]. Remote Sensing, 2014, 6(9): 8524−8540. doi: 10.3390/rs6098524 [109] Kahru M, Mitchell B G. Seasonal and nonseasonal variability of satellite-derived chlorophyll and colored dissolved organic matter concentration in the California Current[J]. Journal of Geophysical Research: Oceans, 2001, 106(C2): 2517−2529. doi: 10.1029/1999JC000094 [110] Sun Kunpeng, Zhang Tinglu, Chen Shuguo, et al. Retrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(6): 4579−4589. doi: 10.1109/TGRS.2020.3020294 [111] Zhou Wen, Cao Wenxi, Zhao Jun, et al. Variability of scattering and backscattering of marine particles in relation to particle concentration, size distribution, and composition off the eastern hainan coast in the south China sea[J]. Continental Shelf Research, 2022, 232: 104615. doi: 10.1016/j.csr.2021.104615 [112] Nazeer M, Nichol J E. Improved water quality retrieval by identifying optically unique water classes[J]. Journal of Hydrology, 2016, 541: 1119−1132. doi: 10.1016/j.jhydrol.2016.08.020 [113] Nazeer M, Nichol J E. Development and application of a remote sensing-based Chlorophyll-a concentration prediction model for complex coastal waters of Hong Kong[J]. Journal of Hydrology, 2016, 532: 80−89. doi: 10.1016/j.jhydrol.2015.11.037 [114] Ma Chunlei, Zhao Jun, Ai Bin, et al. Machine learning based long-term water quality in the turbid Pearl River Estuary, China[J]. Journal of Geophysical Research: Oceans, 2022, 127(1): e2021JC018017. doi: 10.1029/2021JC018017 [115] Liu Huizeng, Wu Guofeng, Shi Tiezhu, et al. Estimating orthophosphate phosphorus concentration in Shenzhen Bay with remote sensing and legacy in-situ measurements[C]//2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA). Guangzhou: IEEE, 2016: 127−131. [116] Reid P C, Colebrook J M, Matthews J B L, et al. The Continuous Plankton Recorder: concepts and history, from Plankton Indicator to undulating recorders[J]. Progress in Oceanography, 2003, 58(2/4): 117−173. [117] Raitsos D E, Reid P C, Lavender S J, et al. Extending the SeaWiFS chlorophyll data set back 50 years in the northeast Atlantic[J]. Geophysical Research Letters, 2005, 32(6): L06603. [118] Batten S D, Walne A W, Edwards M, et al. Phytoplankton biomass from continuous plankton recorder data: an assessment of the phytoplankton colour index[J]. Journal of Plankton Research, 2003, 25(7): 697−702. doi: 10.1093/plankt/25.7.697 [119] Raitsos D E, Lavender S J, Maravelias C D, et al. Macroscale factors affecting diatom abundance: a synergistic use of Continuous Plankton Recorder and satellite remote sensing data[J]. International Journal of Remote Sensing, 2011, 32(8): 2081−2094. doi: 10.1080/01431161003645832 [120] Raitsos D E, Lavender S J, Maravelias C D, et al. Identifying four phytoplankton functional types from space: an ecological approach[J]. Limnology and Oceanography, 2008, 53(2): 605−613. doi: 10.4319/lo.2008.53.2.0605 [121] Head E J H, Pepin P. Monitoring changes in phytoplankton abundance and composition in the Northwest Atlantic: a comparison of results obtained by continuous plankton recorder sampling and colour satellite imagery[J]. Journal of Plankton Research, 2010, 32(12): 1649−1660. doi: 10.1093/plankt/fbq120 [122] Smith R C, Baker K S, Fraser W R, et al. The Palmer LTER: a long-term ecological research program at Palmer Station, Antarctica[J]. Oceanography, 1995, 8(3): 77−86. doi: 10.5670/oceanog.1995.01 [123] Montes‐Hugo M A, Vernet M, Smith R, et al. Phytoplankton size‐structure on the western shelf of the Antarctic Peninsula: a remote‐sensing approach[J]. International Journal of Remote Sensing, 2008, 29(3): 801−829. doi: 10.1080/01431160701297615 [124] Soppa M A, Dinter T, Taylor B B, et al. Satellite derived euphotic depth in the Southern Ocean: implications for primary production modelling[J]. Remote Sensing of Environment, 2013, 137: 198−211. doi: 10.1016/j.rse.2013.06.017 [125] Dierssen H M, Smith R C. Bio-optical properties and remote sensing ocean color algorithms for Antarctic Peninsula waters[J]. Journal of Geophysical Research: Oceans, 2000, 105(C11): 26301−26312. doi: 10.1029/1999JC000296 [126] Marrari M, Hu Chuanmin, Daly K. Validation of SeaWiFS chlorophyll a concentrations in the Southern Ocean: a revisit[J]. Remote Sensing of Environment, 2006, 105(4): 367−375. doi: 10.1016/j.rse.2006.07.008 [127] Kramer S J, Siegel D A. How can phytoplankton pigments be best used to characterize surface ocean phytoplankton groups for ocean color remote sensing algorithms?[J]. Journal of Geophysical Research: Oceans, 2019, 124(11): 7557−7574. doi: 10.1029/2019JC015604 [128] Clark D K, Gordon H R, Voss K J, et al. Validation of atmospheric correction over the oceans[J]. Journal of Geophysical Research: Atmospheres, 1997, 102(D14): 17209−17217. doi: 10.1029/96JD03345 [129] Song Qingjun, Chen Shuguo, Xue Cheng, et al. Vicarious calibration of COCTS-HY1C at visible and near-infrared bands for ocean color application[J]. Optics Express, 2019, 27(20): A1615−A1626. doi: 10.1364/OE.27.0A1615 [130] Wang Menghua, Liu Xiaoming, Jiang Lide, et al. VIIRS ocean color research and applications[C]//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan: IEEE, 2015: 2911-2914. [131] Gerbi G P, Boss E, Werdell P J, et al. Validation of ocean color remote sensing reflectance using autonomous floats[J]. Journal of Atmospheric and Oceanic Technology, 2016, 33(11): 2331−2352. doi: 10.1175/JTECH-D-16-0067.1 [132] Wang Menghua, Son S. VIIRS-derived chlorophyll-a using the ocean color index method[J]. Remote Sensing of Environment, 2016, 182: 141−149. doi: 10.1016/j.rse.2016.05.001 [133] Lawson A, Bowers J, Ladner S, et al. Analyzing satellite ocean color match-up protocols using the satellite validation navy tool (SAVANT) at MOBY and two AERONET-OC sites[J]. Remote Sensing, 2021, 13(14): 2673. doi: 10.3390/rs13142673 [134] Gilerson A, Herrera-Estrella E, Foster R, et al. Determining the primary sources of uncertainty in retrieval of marine remote sensing reflectance from satellite ocean color sensors[J]. Frontiers in Remote Sensing, 2022, 3: 857530. doi: 10.3389/frsen.2022.857530 [135] Gilerson A, Herrera-Estrella E, Agagliate J, et al. Determining the primary sources of uncertainty in the retrieval of marine remote sensing reflectance from satellite ocean color sensors II. Sentinel 3 OLCI sensors[J]. Frontiers in Remote Sensing, 2023, 4: 1146110. doi: 10.3389/frsen.2023.1146110 [136] Wang Menghua, Wilson C. Applications of satellite ocean color products[C]//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth: IEEE, 2017: 2794-2797. [137] Antoine D, D’Ortenzio F, Hooker S B, et al. Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS-A) at an offshore site in the Mediterranean Sea (BOUSSOLE project)[J]. Journal of Geophysical Research: Oceans, 2008, 113(C7): C07013. [138] Organelli E, Bricaud A, Antoine D, et al. Seasonal dynamics of light absorption by chromophoric dissolved organic matter (CDOM) in the NW Mediterranean Sea (BOUSSOLE site)[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2014, 91: 72−85. doi: 10.1016/j.dsr.2014.05.003 [139] Organelli E, Bricaud A, Antoine D, et al. Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site)[J]. Applied Optics, 2013, 52(11): 2257−2273. doi: 10.1364/AO.52.002257 [140] Kheireddine M, Antoine D. Diel variability of the beam attenuation and backscattering coefficients in the northwestern Mediterranean Sea (BOUSSOLE site)[J]. Journal of Geophysical Research: Oceans, 2014, 119(8): 5465−5482. doi: 10.1002/2014JC010007 [141] Navarro G, Alvain S, Vantrepotte V, et al. Identification of dominant phytoplankton functional types in the Mediterranean Sea based on a regionalized remote sensing approach[J]. Remote Sensing of Environment, 2014, 152: 557−575. doi: 10.1016/j.rse.2014.06.029 [142] Navarro G, Almaraz P, Caballero I, et al. Reproduction of spatio-temporal patterns of major mediterranean phytoplankton groups from remote sensing OC-CCI data[J]. Frontiers in Marine Science, 2017, 4: 246. doi: 10.3389/fmars.2017.00246 [143] Zibordi G, Mélin F, Berthon J F, et al. AERONET-OC: a network for the validation of ocean color primary products[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(8): 1634−1651. doi: 10.1175/2009JTECHO654.1 [144] Zibordi G, Holben B, Hooker S B, et al. A network for standardized ocean color validation measurements[J]. Eos, Transactions American Geophysical Union, 2006, 87(30): 293−297. [145] Nobileau D, Antoine D. Detection of blue-absorbing aerosols using near infrared and visible (ocean color) remote sensing observations[J]. Remote Sensing of Environment, 2005, 95(3): 368−387. doi: 10.1016/j.rse.2004.12.020 [146] Schroeder T, Schaale M, Fischer J. Retrieval of atmospheric and oceanic properties from MERIS measurements: a new Case‐2 water processor for BEAM[J]. International Journal of Remote Sensing, 2007, 28(24): 5627−5632. doi: 10.1080/01431160701601774 [147] Ahmad Z, Franz B A, Mcclain C R, et al. New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans[J]. Applied Optics, 2010, 49(29): 5545−5560. doi: 10.1364/AO.49.005545 [148] He Xianqiang, Pan Delu, Bai Yan, et al. Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans[J]. Applied Optics, 2011, 50(22): 4353−4364. doi: 10.1364/AO.50.004353 [149] Martins V S, Barbosa C C F, De Carvalho L A S, et al. Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to amazon floodplain lakes[J]. Remote Sensing, 2017, 9(4): 322. doi: 10.3390/rs9040322 [150] Kravitz J, Matthews M, Bernard S, et al. Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: successes and challenges[J]. Remote Sensing of Environment, 2020, 237: 111562. doi: 10.1016/j.rse.2019.111562 [151] Vanhellemont Q, Ruddick K. Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications[J]. Remote Sensing of Environment, 2018, 216: 586−597. doi: 10.1016/j.rse.2018.07.015 [152] Zhang Minwei, Hu Chuanmin, Barnes B B. Performance of POLYMER atmospheric correction of ocean color imagery in the presence of absorbing aerosols[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6666−6674. doi: 10.1109/TGRS.2019.2907884 [153] Wolters E, Toté C, Sterckx S, et al. iCOR atmospheric correction on Sentinel-3/OLCI over land: intercomparison with AERONET, RadCalNet, and SYN Level-2[J]. Remote Sensing, 2021, 13(4): 654. doi: 10.3390/rs13040654 [154] Zibordi G, Berthon J F, Mélin F, et al. Validation of satellite ocean color primary products at optically complex coastal sites: northern Adriatic Sea, northern Baltic Proper and Gulf of Finland[J]. Remote Sensing of Environment, 2009, 113(12): 2574−2591. doi: 10.1016/j.rse.2009.07.013 [155] Goyens C, Jamet C, Schroeder T. Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters[J]. Remote Sensing of Environment, 2013, 131: 63−75. doi: 10.1016/j.rse.2012.12.006 [156] Pahlevan N, Schott J R, Franz B A, et al. Landsat 8 remote sensing reflectance (Rrs) products: evaluations, intercomparisons, and enhancements[J]. Remote Sensing of Environment, 2017, 190: 289−301. doi: 10.1016/j.rse.2016.12.030 [157] Steinmetz F, Ramon D. Sentinel-2 MSI and Sentinel-3 OLCI consistent ocean colour products using POLYMER[C]//Remote Sensing of the Open and Coastal Ocean and Inland Waters. Honolulu: SPIE, 2018. [158] Fan Yongzhen, Li Wei, Chen Nan, et al. OC-SMART: a machine learning based data analysis platform for satellite ocean color sensors[J]. Remote Sensing of Environment, 2021, 253: 112236. doi: 10.1016/j.rse.2020.112236 [159] Ye Xiaomin, Liu Jianqiang, Lin Mingsen, et al. Global ocean chlorophyll-a concentrations derived from COCTS onboard the HY-1C satellite and their preliminary evaluation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(12): 9914−9926. doi: 10.1109/TGRS.2020.3036963 [160] Wang Junwei, Wang Yongchao, Lee Z, et al. A revision of NASA SeaDAS atmospheric correction algorithm over turbid waters with artificial Neural Networks estimated remote-sensing reflectance in the near-infrared[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 194: 235−249. doi: 10.1016/j.isprsjprs.2022.10.014 [161] Jamet C, Loisel H, Kuchinke C P, et al. Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements[J]. Remote Sensing of Environment, 2011, 115(8): 1955−1965. doi: 10.1016/j.rse.2011.03.018 [162] Müller D, Krasemann H, Brewin R J W, et al. The Ocean Colour Climate Change Initiative: I. A methodology for assessing atmospheric correction processors based on in-situ measurements[J]. Remote Sensing of Environment, 2015, 162: 242−256. doi: 10.1016/j.rse.2013.11.026 [163] Bittig H C, Maurer T L, Plant J N, et al. A BGC-Argo guide: planning, deployment, data handling and usage[J]. Frontiers in Marine Science, 2019, 6: 502. doi: 10.3389/fmars.2019.00502 [164] Xing Xiaogang, Boss E, Zhang Jie, et al. Evaluation of ocean color remote sensing algorithms for diffuse attenuation coefficients and optical depths with data collected on BGC-Argo floats[J]. Remote Sensing, 2020, 12(15): 2367. doi: 10.3390/rs12152367 [165] Boss E, Swift D, Taylor L, et al. Observations of pigment and particle distributions in the western North Atlantic from an autonomous float and ocean color satellite[J]. Limnology and Oceanography, 2008, 53(5part2): 2112−2122. doi: 10.4319/lo.2008.53.5_part_2.2112 [166] Haëntjens N, Boss E, Talley L D. Revisiting Ocean Color algorithms for chlorophyll a and particulate organic carbon in the Southern Ocean using biogeochemical floats[J]. Journal of Geophysical Research: Oceans, 2017, 122(8): 6583−6593. doi: 10.1002/2017JC012844 [167] Gittings J A, Raitsos D E, Kheireddine M, et al. Evaluating tropical phytoplankton phenology metrics using contemporary tools[J]. Scientific Reports, 2019, 9(1): 674. doi: 10.1038/s41598-018-37370-4 [168] Wojtasiewicz B, Hardman-Mountford N J, Antoine D, et al. Use of bio-optical profiling float data in validation of ocean colour satellite products in a remote ocean region[J]. Remote Sensing of Environment, 2018, 209: 275−290. doi: 10.1016/j.rse.2018.02.057 [169] Terzić E, Miró A, Organelli E, et al. Radiative transfer modeling with biogeochemical-argo float data in the Mediterranean Sea[J]. Journal of Geophysical Research: Oceans, 2021, 126(10): e2021JC017690. doi: 10.1029/2021JC017690 [170] Xing Xiaogang, Boss E. Chlorophyll-based model to estimate underwater photosynthetically available radiation for modeling, in-situ, and remote-sensing applications[J]. Geophysical Research Letters, 2021, 48(7): e2020GL092189. doi: 10.1029/2020GL092189 [171] Wang Bin, Fennel K, Yu Liuqian, et al. Assessing the value of biogeochemical Argo profiles versus ocean color observations for biogeochemical model optimization in the Gulf of Mexico[J]. Biogeosciences, 2020, 17(15): 4059−4074. doi: 10.5194/bg-17-4059-2020 [172] Wang Bin, Fennel K. Biogeochemical-Argo data suggest significant contributions of small particles to the vertical carbon flux in the subpolar North Atlantic[J]. Limnology and Oceanography, 2022, 67(11): 2405−2417. doi: 10.1002/lno.12209 [173] Johnson K S, Bif M B. Constraint on net primary productivity of the global ocean by Argo oxygen measurements[J]. Nature Geoscience, 2021, 14(10): 769−774. doi: 10.1038/s41561-021-00807-z [174] 邱国强, 王海黎, 邢小罡. BGC-Argo浮标观测在海洋生物地球化学中的应用[J]. 厦门大学学报(自然科学版), 2018, 57(6): 827−840.Qiu Guoqiang, Wang Haili, Xing Xiaogang. Application of BGC-Argo floats observation to ocean biogeochemistry[J]. Journal of Xiamen University (Natural Science), 2018, 57(6): 827−840. [175] Addey C I. Using Biogeochemical Argo floats to understand ocean carbon and oxygen dynamics[J]. Nature Reviews Earth & Environment, 2022, 3(11): 739. [176] Davis C O, Tufillaro N, Nahorniak J, et al. Evaluating VIIRS ocean color products for west coast and Hawaiian waters[C]//Ocean Sensing and Monitoring V. Baltimore: SPIE, 2013: 87240J. [177] Westberry T, Behrenfeld M J, Siegel D A, et al. Carbon-based primary productivity modeling with vertically resolved photoacclimation[J]. Global Biogeochemical Cycles, 2008, 22(2): GB2024. [178] Westberry T K, Silsbe G M, Behrenfeld M J. Gross and net primary production in the global ocean: an ocean color remote sensing perspective[J]. Earth-Science Reviews, 2023, 237: 104322. doi: 10.1016/j.earscirev.2023.104322 [179] Gardner W D, Mishonov A V, Richardson M J. Global POC concentrations from in-situ and satellite data[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2006, 53(5/7): 718−740. [180] Loisel H, Nicolas J M, Deschamps P Y, et al. Seasonal and inter-annual variability of particulate organic matter in the global ocean[J]. Geophysical Research Letters, 2002, 29(24): 2196. [181] Nelson N B, Siegel D A, Michaels A F. Seasonal dynamics of colored dissolved material in the Sargasso Sea[J]. Deep Sea Research Part I: Oceanographic Research Papers, 1998, 45(6): 931−957. doi: 10.1016/S0967-0637(97)00106-4 [182] Neuer S, Cianca A, Helmke P, et al. Biogeochemistry and hydrography in the eastern subtropical North Atlantic gyre. Results from the European time-series station ESTOC[J]. Progress in Oceanography, 2007, 72(1): 1−29. doi: 10.1016/j.pocean.2006.08.001 [183] Davenport R, Neuer S, Hernandez-Guerra A, et al. Seasonal and interannual pigment concentration in the Canary Islands region from CZCS data and comparison with observations from the ESTOC[J]. International Journal of Remote Sensing, 1999, 20(7): 1419−1433. doi: 10.1080/014311699212803 [184] Neuer S, Ratmeyer V, Davenport R, et al. Deep water particle flux in the Canary Island region: seasonal trends in relation to long-term satellite derived pigment data and lateral sources[J]. Deep Sea Research Part I: Oceanographic Research Papers, 1997, 44(8): 1451−1466. doi: 10.1016/S0967-0637(97)00034-4 [185] Davenport R, Neuer S, Helmke P, et al. Primary productivity in the northern Canary Islands region as inferred from SeaWiFS imagery[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2002, 49(17): 3481−3496. doi: 10.1016/S0967-0645(02)00095-4 [186] Helmke P, Neuer S, Lomas M W, et al. Cross-basin differences in particulate organic carbon export and flux attenuation in the subtropical North Atlantic gyre[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2010, 57(2): 213−227. doi: 10.1016/j.dsr.2009.11.001 [187] Stramska M. The diffusive component of particulate organic carbon export in the North Atlantic estimated from SeaWiFS ocean color[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2010, 57(2): 284−296. doi: 10.1016/j.dsr.2009.11.007 [188] Song Qingjun, Chen Shuguo, Hu Lianbo, et al. Introducing two fixed platforms in the Yellow Sea and East China Sea supporting long-term satellite ocean color validation: preliminary data and results[J]. Remote Sensing, 2022, 14(12): 2894. doi: 10.3390/rs14122894 [189] 史鑫皓, 陈树果, 林明森, 等. 中国海洋水色卫星传感器COCTS HY-1D产品初步评价[J]. 遥感学报, 2023, 27(4): 943−952. doi: 10.11834/jrs.20221666Shi Xinhao, Chen Shuguo, Lin Mingsen, et al. Preliminary performance of the COCTS onboard HY-1D satellite in the global ocean[J]. National Remote Sensing Bulletin, 2023, 27(4): 943−952. doi: 10.11834/jrs.20221666 [190] Field C B, Behrenfeld M J, Randerson J T, et al. Primary production of the biosphere: integrating terrestrial and oceanic components[J]. Science, 1998, 281(5374): 237−240. doi: 10.1126/science.281.5374.237 [191] Mueller J L, Morel A, Frouin R, et al. Ocean optics protocols for satellite ocean color sensor validation, Revision 4[R]. Greenbelt, MD: Goddard Space Flight Space Center, 2003: 1−63. [192] IOCCG. IOCCG ocean optics and biogeochemistry protocols for satellite ocean colour sensor validation[R]. Dartmouth: International Ocean Colour Coordinating Group (IOCCG), 2018−2024. -