{
    "created": "2025-01-23 11:51:17",
    "updated": "2026-04-28 17:39:53",
    "id": "3fc0f146-3f29-4036-934a-16c78c8e569f",
    "version": 7,
    "ds_topic": null,
    "title_cn": "中国每日 1 公里无间隙含氧量网格数据集（2000-2020 年）",
    "title_en": "Daily 1-km gap-free AOD grids in China(2000–2020)",
    "ds_abstract": "<p>&emsp;&emsp;长时序无间隙高分辨率空气污染物浓度数据集（简称LGHAP）对于环境管理和地球系统科学分析具有重要意义。本数据集为2000-2020 年中国陆地区域的无间隙 AOD 产品每日数据，分辨率为 1 千米。通过将基于张量流的多模态数据融合与统计数据挖掘中基于集合学习的知识转移进行无缝整合生成。所提出的方法通过空间模式识别，将从不同传感器或平台获取的一组 AOD 数据张量和其他相关数据集（如空气污染物浓度和大气能见度）进行整合，用于高维网格数据分析，从而实现数据融合和多分辨率图像分析。每日无间隙 AOD 以 NetCDF 格式提供，各年数据以压缩文件形式存档。此外，还提供了 Python、Matlab、R 和 IDL 代码，以帮助用户读取和可视化 LGHAP 数据。",
    "ds_source": "<p>&emsp;&emsp;本数据来源于Zenodo网站（https://zenodo.org/records/5652257）。",
    "ds_process_way": "<p>&emsp;&emsp;本数据集通过将基于张量流的多模态数据融合与统计数据挖掘中基于集合学习的知识转移进行无缝整合生成。所提出的方法通过空间模式识别，将从不同传感器或平台获取的一组 AOD 数据张量和其他相关数据集（如空气污染物浓度和大气能见度）进行整合，用于高维网格数据分析，从而实现数据融合和多分辨率图像分析。",
    "ds_quality": "<p>&emsp;&emsp;数据质量较好。",
    "ds_acq_start_time": "2000-01-01 00:00:00",
    "ds_acq_end_time": "2020-12-31 00:00:00",
    "ds_acq_place": "中国",
    "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": 26175863688,
    "ds_files_count": 23,
    "ds_format": "NetCDF",
    "ds_space_res": "1000m",
    "ds_time_res": "日",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "3fc0f146-3f29-4036-934a-16c78c8e569f.png",
    "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": [
        "170.1515"
    ],
    "quality_level": 3,
    "publish_time": "2025-01-24 10:10:27",
    "last_updated": "2026-01-14 11:03:56",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ZENODO.DB6748.2025",
    "i18n": {
        "en": {
            "title": "Daily 1-km gap-free AOD grids in China(2000–2020)",
            "ds_format": "NetCDF",
            "ds_source": "<p>&emsp;This data is sourced from the Zenodo website（ https://zenodo.org/records/5652257 ）.",
            "ds_quality": "<p>&emsp;The data quality is good.",
            "ds_ref_way": "",
            "ds_abstract": "<p> The long-term gapless high-resolution air pollutant concentration dataset (LGHAP) is of great significance for environmental management and Earth system science analysis. This dataset contains daily data of seamless AOD products in China's land region from 2000 to 2020, with a resolution of 1 kilometer. By seamlessly integrating tensor flow based multimodal data fusion with set learning based knowledge transfer in statistical data mining. The proposed method integrates a group of AOD data tensors obtained from different sensors or platforms and other relevant data sets (such as air pollutant concentration and atmospheric visibility) for high-dimensional grid data analysis through spatial pattern recognition, so as to achieve data fusion and multi-resolution image analysis. Daily seamless AOD is provided in NetCDF format, and data from each year is archived in compressed file format. In addition, Python, Matlab, R, and IDL code are provided to assist users in reading and visualizing LGHAP data.</p>",
            "ds_time_res": "日",
            "ds_acq_place": "China",
            "ds_space_res": "1000m",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp;The dataset was generated via a seamless integration of the tensor flow based multimodal data fusion with ensemble learning based knowledge transfer in statistical data mining. The proposed method transformed a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility that were acquired from diversified sensors or platforms via integrative efforts of spatial pattern recognition for high dimensional gridded data analysis toward data fusion and multiresolution image analysis.",
            "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": [
        "AOD",
        "空气污染",
        "LGHAP"
    ],
    "ds_subject_tags": [
        "大气化学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "中国"
    ],
    "ds_time_tags": [
        2000,
        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": "白开旭",
            "email": "kxbai@geo.ecnu.edu.cn",
            "work_for": "华东师范大学地理科学学院地理信息科学教育部重点实验室",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "白开旭",
            "email": "kxbai@geo.ecnu.edu.cn",
            "work_for": "华东师范大学地理科学学院地理信息科学教育部重点实验室",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "白开旭",
            "email": "kxbai@geo.ecnu.edu.cn",
            "work_for": "华东师范大学地理科学学院地理信息科学教育部重点实验室",
            "country": "中国"
        }
    ],
    "category": "气象"
}