利用信息提取方法对欧洲航天局Sentinel-1在北极地区的SAR数据进行分析，得到相应的数据产品。哨兵- 1A和1B卫星自2014年和2016年发射以来，在极地地区广泛获取了超宽(EW)带状模式数据。这对双胞胎可以在北极每月获得大约2500个场景的SAR数据。基于上述方法，我们对在北极获取的每张S1 SAR图像进行处理，生成NetCDF标准cf1.7公约下的海冰覆盖数据。", "ds_source": "
欧洲航天局Sentinel-1数据。", "ds_process_way": "
基于深度学习的U-Net架构，提出一种基于Sentinel-1 SAR数据的高分辨率海冰覆盖信息提取方法。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2019-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "北极地区", "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": 1102535403685, "ds_files_count": 38, "ds_format": "nc", "ds_space_res": "400", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "f66aea00-c73a-45e8-9366-4f02ffa008aa.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "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": "2023-12-27 10:47:30", "last_updated": "2026-01-14 10:54:38", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.00273", "i18n": { "en": { "title": "Arctic Sea Ice Coverage Products Based on Spaceborne Synthetic Aperture Radar (2019-2021)", "ds_format": "nc", "ds_source": "
Sentinel-1 data from the European Space Agency.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Using information extraction methods to analyze SAR data of Sentinel-1 from the European Space Agency in the Arctic region and obtain corresponding data products. Since their launch in 2014 and 2016, Sentinel-1A and 1B satellites have extensively obtained ultra wide (EW) band pattern data in polar regions. These twins can obtain SAR data for approximately 2500 scenes per month in the Arctic. Based on the above methods, we processed each S1 SAR image obtained in the Arctic to generate sea ice cover data under the NetCDF standard cf1.7 convention.
", "ds_time_res": "年", "ds_acq_place": "Arctic region", "ds_space_res": "400", "ds_projection": "", "ds_process_way": "A high-resolution sea ice cover information extraction method based on Sentinel-1 SAR data is proposed using the U-Net architecture of deep learning.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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": [ "海冰覆盖", "合成孔径雷达", "极地" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极地区" ], "ds_time_tags": [ 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "李晓明", "email": "lixm@radi.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李晓明", "email": "lixm@radi.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "李晓明", "email": "lixm@radi.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "category": "极地" }