海洋颜色数据对于理解生物和生态现象过程的至关重要，也是海洋物理和生物地球化学模型的重要输入来源。在海洋环境中，叶绿素-a (Chl-a)是海洋颜色的关键变量。考虑到与海洋浮游植物生长和分布相关的环境变量，开发了一个名为OCNET的卷积神经网络(CNN)，用于在开阔海域重建Chl-a浓度数据。", "ds_source": "
再分析资料和卫星观测的海面温度(SST)、盐度(SAL)、光合有效辐射(PAR)和海面压力(SSP)数据。", "ds_process_way": "
OCNET以再分析资料和卫星观测的海面温度(SST)、盐度(SAL)、光合有效辐射(PAR)和海面压力(SSP)作为输入，与环境和浮游植物质量进行关联。所建立的OCNET模型在全球海洋Chl-a浓度数据的重建中取得了较好的效果，并捕获了这些特征的时间变化。", "ds_quality": "
数据质量较好。", "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": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 17203742410, "ds_files_count": 22, "ds_format": "nc", "ds_space_res": "0.25°", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "5e2b04f6-6a38-4c36-8aa1-faa43871b4b8.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "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": [ "170.6040" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:17", "last_updated": "2026-01-14 11:22:11", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6671.2024", "i18n": { "en": { "title": "OCNET Global Daily Chlorophyl-a Product Dataset (2001-2021)", "ds_format": "nc", "ds_source": "
Further analyze data and satellite observations of sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP).", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Ocean color data is crucial for understanding biological and ecological processes, and is also an important input source for ocean physics and biogeochemical models. In the marine environment, chlorophyll-a (Chl-a) is a key variable for ocean color. Considering the environmental variables related to the growth and distribution of marine phytoplankton, a convolutional neural network (CNN) called OCNET was developed for reconstructing Chl-a concentration data in open sea areas.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "0.25°", "ds_projection": "", "ds_process_way": "OCNET uses reanalysis data and satellite observations of sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) as inputs to correlate with the environment and phytoplankton mass. The established OCNET model has achieved good results in the reconstruction of global ocean Chl-a concentration data and captured the temporal variations of these features.", "ds_ref_instruction": "" } }, "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": [ "OCNET", "海洋颜色", "叶绿素-a" ], "ds_subject_tags": [ "海洋生物学" ], "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" }