水生植被对改善水质、支持渔业和保护湖泊生物多样性至关重要。监测水生植被的时空动态对于评估和保护湖泊生态系统不可或缺。然而，目前还缺乏对湖泊水生植被的全球综合评估。本研究为哨兵-2 MSI 引入了一种自动识别算法（总准确率达 94.4%），从而首次利用 1480 万张图像绘制了 2019 年至 2022 年期间 140 万个湖泊的全球水生植被分布图。结果显示，在过去四年中，六大洲的81116个湖泊中出现了水生植被，累计最大水生植被面积（MVA）为16111.8KM2。全球水生植被发生率中位数（VO，%）为 3.0%，南美洲（7.4%）和非洲（4.1%）的数值明显高于亚洲（2.7%）和北美洲（2.4%）。在长江、鄂毕河和巴拉那河等主要河流附近的湖泊中，也观察到了较高的 VO 值。将历史数据与我们计算的水生植被覆盖率相结合，分析了全球 170 个湖泊的水生植被变化。结果显示，从 20 世纪 80 年代初到 2022 年，72.4% 的湖泊（123/170）经历了水生植被的减少，包括沉水植被和整体水生植被。亚洲和非洲的降幅最大。我们的研究结果表明，除了湖泊藻华和温度之外，湖泊的物理特征及其周围环境也会影响水生植被的分布。我们的研究为保护和恢复湖泊水生植被提供了宝贵的信息。
", "ds_source": "研究利用了2019年至2022年的哨兵-2A和哨兵-2B MSI Level-2地表反射率（SR）产品，共计1480万幅图像。
", "ds_process_way": "本研究为哨兵-2 MSI 引入了一种自动识别算法（总准确率为 94.4%），从而首次利用 1480 万张图像绘制了 2019 年至 2022 年期间 140 万个湖泊的全球水生植被分布图。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2019-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 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": "open-access", "ds_total_size": 35655063, "ds_files_count": 2, "ds_format": "", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "6489d720-7d51-4f55-9892-300c0ac4e8d9.jpg", "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": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "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": "2024-12-27 15:58:07", "last_updated": "2025-05-29 11:33:08", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6696.2024", "i18n": { "en": { "title": "Global lake aquatic vegetation datasets", "ds_format": "", "ds_source": "This study utilized Sentinel-2A and Sentinel-2B MSI Level-2 surface reflectance (SR) products from 2019 to 2022, totalling 14.8 million images.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 KM2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022.
", "ds_ref_instruction": "When 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": [ "水生植被", "MVA", "VO" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "侯雪姣", "email": "houxj6@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "侯雪姣", "email": "houxj6@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" } ], "ds_managers": [ { "true_name": "侯雪姣", "email": "houxj6@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" } ], "category": "水文" }