海洋颜色数据对我们了解生物和生态现象及过程至关重要，也是物理和生物地球化学海洋模型的重要输入源。叶绿素-a（Chl-a）是海洋环境中海洋颜色的关键变量。卫星遥感定量检索是获取大尺度海洋Chl-a的主要方法。然而，数据缺失是基于卫星遥感的Chl-a产品的主要局限性，这主要是由于云层、太阳微光污染和高卫星视角的影响。重建（填补）缺失数据的常用方法通常只考虑初始图像的时空信息，如数据插值经验正交函数、最优插值、克里金插值和扩展卡尔曼滤波等。然而，这些方法在图像中存在大规模缺失值的情况下表现不佳，而且忽略了缺失像素的其他信息在数据重建中的潜力。考虑到与海洋浮游植物生长和分布相关的环境变量，我们开发了一种名为OCNET的卷积神经网络（CNN），用于开阔海域的Chl-a浓度数据重建。从再分析数据和卫星观测数据中选取海面温度（SST）、盐度（SAL）、光合有效辐射（PAR）和海面气压（SSP）作为OCNET的输入变量，与环境和浮游植物数量相关联。所开发的OCNET模型在重建全球海洋Chl-a浓度数据方面取得了良好的性能，并捕捉到了这些特征的时间变化。全球Chl-a数据集涵盖 2001 年至 2021 年，时间分辨率为日，空间分辨率为 0.25°。
", "ds_source": "海洋-颜色气候变化倡议（OCCCI）第 5 版和美国国家海洋和大气管理局多传感器 DINEOF 全球填隙数据（以下简称 NOAA MSL12）是用于训练 OCNET 模型的两个 Chl-a 产品。OCCCI 的数据来源包括欧洲航天局的中光谱分辨率成像分光仪（MERIS）传感器、美国国家航空航天局的 SeaWiFS（海洋观测宽视场传感器）和 MODIS-Aqua（中分辨率成像分光仪-Aqua）传感器，以及美国国家海洋和大气管理局的 VIIRS 传感器（可见光和红外成像辐射计套件）。从再分析数据和卫星观测数据中选取海面温度（SST）、盐度（SAL）、光合有效辐射（PAR）和海面气压（SSP）作为 OCNET 的输入变量，与环境和浮游植物数量相关联。
", "ds_process_way": "鉴于CNN适用于卫星遥感图像和气候模型数据，我们构建了由405个区域CNN组成的全球OCNET模型。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 17203742410, "ds_files_count": 22, "ds_format": "netCDF", "ds_space_res": " 0.25°", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "80103364-3ef7-45fa-b5be-1e0eec0b6a1f.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-03-26 14:00:33", "last_updated": "2025-06-30 16:25:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6456.2024", "i18n": { "en": { "title": "OCNET Global Daily Chlorophyl-a Product Dataset (2001-2021)", "ds_format": "netCDF", "ds_source": "The Ocean Color Climate Change Initiative (OCCCI) 5th edition and the National Oceanic and Atmospheric Administration Multi Sensor DINEOF Global Gap Data (NOAA MSL12) are two Chl-a products used for training OCNET models. The data sources of OCCCI include the European Space Agency's Medium Resolution Imaging Spectroradiometer (MERIS) sensor, NASA's SeaWiFS (Wide Field of View for Ocean Observations) and MODIS Aqua (Medium Resolution Imaging Spectroradiometer Aqua) sensors, and the National Oceanic and Atmospheric Administration's VIIRS sensor (Visible and Infrared Imaging Radiometer Suite). Select sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) as input variables for OCNET from reanalysis data and satellite observation data, and correlate them with the environment and phytoplankton population.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Ocean color data is crucial for our understanding of biological and ecological phenomena and processes, and is also an important input source for physical and biogeochemical ocean models. Chlorophyl-a (Chl-a) is a key variable for ocean color in marine environments. Satellite remote sensing quantitative retrieval is the main method for obtaining large-scale ocean Chl-a. However, data loss is the main limitation of Chl-a products based on satellite remote sensing, mainly due to the influence of cloud cover, solar low light pollution, and high satellite perspective. The common methods for reconstructing (filling) missing data usually only consider the spatiotemporal information of the initial image, such as empirical orthogonal functions for data interpolation, optimal interpolation, kriging interpolation, and extended Kalman filtering. However, these methods perform poorly in the presence of large-scale missing values in the image, and ignore the potential of other information about missing pixels in data reconstruction. Considering the environmental variables related to the growth and distribution of marine phytoplankton, we developed a convolutional neural network (CNN) called OCNET for the reconstruction of Chl-a concentration data in open waters. Select sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) as input variables for OCNET from reanalysis data and satellite observation data, and correlate them with the environment and phytoplankton population. The developed OCNET model has achieved good performance in reconstructing global ocean Chl-a concentration data and captured the temporal variations of these features. The global Chl-a dataset covers the period from 2001 to 2021, with a temporal resolution of days and a spatial resolution of 0.25 °.
", "ds_time_res": "日", "ds_acq_place": "Global", "ds_space_res": " 0.25°", "ds_projection": "", "ds_process_way": "Given that CNN is suitable for satellite remote sensing images and climate model data, we have constructed a global OCNET model consisting of 405 regional CNNs.
", "ds_ref_instruction": "\r\nWhen using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "叶绿素-a", "卫星遥感定量检索", "OCNET" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "龙笛", "email": "dlong@tsinghua.edu.cn", "work_for": "清华大学水利工程系水利科学与工程国家重点实验室，北京", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "龙笛", "email": "dlong@tsinghua.edu.cn", "work_for": "清华大学水利工程系水利科学与工程国家重点实验室，北京", "country": "中国" } ], "ds_managers": [ { "true_name": "龙笛", "email": "dlong@tsinghua.edu.cn", "work_for": "清华大学水利工程系水利科学与工程国家重点实验室，北京", "country": "中国" } ], "category": "生态" }