将Sentinel-1数据、GF-3卫星SAR和SDGSAT-1热红外数据相结合，获得海冰边缘区多时间间隔观测，时间间隔从几分钟到几十小时间隔，时空范围涵盖了2021年8月-2022年8月及2023年10-12月北极喀拉海、波弗特海和格陵兰海的重点区域，其网格大小为10km*10km。采用面向对象的分割和阈值方法获得Sentinel-1和Sentinel-1的图像对，Sentinel-1和GF-3 图像对、Sentinel-1和SDGSAT-1 TIR图像对的冰水分类图，这些图像对的时间间隔分布在1分钟到72小时之间，在10km网格的样本上GF-3 SAR 数据的空间分辨率为 25m，SDGSAT-1 数据的空间分辨率为 30m，Sentinel-1数据的空间分辨率为 40m。\n
数据命名方式：区域_时间间隔_时间_网格编号_卫星_分辨率.tif\n
属性信息：\n
0：水\n
1：冰", "ds_source": "
1.Sentinel-1数据来源为公开可下载的卫星影像数据，下载地址如下:Sentinel-1：Alaska Satellite Facility (ASF)（https://search.asf.alaska.edu/#）\n
2.GF-3和SDGSAT-1卫星的数据为国产卫星数据（相关数据已通过中国遥感卫星地面站接收）\n
GF-3：https://logindataservices.ceode.ac.cn/cas/login?service=http://ids.ceode.ac.cn/gfds/gflogin\n
SDGSAT-1：http://124.16.184.48:6008/query", "ds_process_way": "
1.数据预处理：对GF-3 L2级SAR数据进行辐射定标，在轨拼接等预处理；Sentinel-1 EW模式数据的处理包括辐射校准、入射角校正和重投影，以产生与GF-3数据相匹配的地理数据；SDGSAT-1 TIS数据的预处理包括辐射定标、温度转换和重投影。\n
2.影像匹配裁剪：对Sentinel-1、GF-3 SAR和SDGSAT-1 TIS预处理后的数据进行裁剪重叠区域，形成Sentinel-1和GF-3 、Sentinel-1和Sentinel-1，Sentinel-1和SDGSAT-1 在一定时间间隔内同一覆盖范围的样本对。\n
3.影像分类：利用面向对象和阈值分割的方法对这些区域影像进行冰水分类，将水赋值为0，冰赋值为1，生成10 km×10 km网格化的冰水二值化分类样本对，得到最终的本数据集。", "ds_quality": "
Sentinel-1分类结果的总体精度为98.20%，kappa系数为0.96;GF-3分类结果的总体精度为95.58%，kappa系数为0.89；SDGSAT-1分类结果的总体精度为84%，kappa系数为0.68。", "ds_acq_start_time": "2021-08-12 00:00:00", "ds_acq_end_time": "2023-10-12 00:00:00", "ds_acq_place": "北极喀拉海,波弗特海,格陵兰海", "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": 270226729, "ds_files_count": 57, "ds_format": "", "ds_space_res": "25m,30m,40m", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "03da632f-ab16-4093-b529-db259fb5af2a.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-05-24 16:55:31", "last_updated": "2025-04-29 16:16:42", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6496.2024", "i18n": { "en": { "title": "Grid based high-resolution ice water classification dataset for Arctic sea ice edge zone (2021)", "ds_format": "", "ds_source": "
The Sentinel-1 data is sourced from publicly available satellite imagery data, and the download link is as follows: Sentinel-1：Alaska Satellite Facility (ASF)（ https://search.asf.alaska.edu/# ）\n
The data from GF-3 and SDGSAT-1 satellites are domestic satellite data (the relevant data has been received through the Chinese remote sensing satellite ground station)\n
GF-3： https://logindataservices.ceode.ac.cn/cas/login?service=http://ids.ceode.ac.cn/gfds/gflogin\n
SDGSAT-1： http://124.16.184.48:6008/query", "ds_quality": "
The overall accuracy of Sentinel-1 classification results is 98.20%, with a kappa coefficient of 0.96; The overall accuracy of GF-3 classification results is 95.58%, with a kappa coefficient of 0.89; The overall accuracy of SDGSAT-1 classification results is 84%, with a kappa coefficient of 0.68.", "ds_ref_way": "", "ds_abstract": "
By combining Sentinel-1 data, GF-3 satellite SAR, and SDGSAT-1 thermal infrared data, multiple time interval observations of the sea ice edge zone were obtained. The time intervals ranged from a few minutes to several tens of hours, covering key areas of the Arctic Kara Sea, Beaufort Sea, and Greenland Sea from August 2021 to August 2022 and from October to December 2023. The grid size was 10km * 10km. Using object-oriented segmentation and thresholding methods to obtain ice water classification maps for Sentinel-1 and Sentinel-1 image pairs, Sentinel-1 and GF-3 image pairs, and Sentinel-1 and SDGSAT-1 TIR image pairs, with time intervals ranging from 1 minute to 72 hours. The spatial resolution of GF-3 SAR data is 25m, SDGSAT-1 data is 30m, and Sentinel-1 data is 40m on a 10km grid sample.\n
Data naming convention: region_time interval time_time grid numbering satellite resolution. tif\n
Attribute information:\n
0: Water\n
1: Ice
", "ds_time_res": "", "ds_acq_place": "Arctic Kara Sea, Beaufort Sea, Greenland Sea", "ds_space_res": "25m,30m,40m", "ds_projection": "", "ds_process_way": "1. Data preprocessing: Perform radiometric calibration, in orbit stitching, and other preprocessing on GF-3 L2 SAR data; The processing of Sentinel-1 EW mode data includes radiometric calibration, incident angle correction, and reprojection to generate geographic data that matches GF-3 data; The preprocessing of SDGSAT-1 TIS data includes radiometric calibration, temperature conversion, and reprojection.\n
2. Image matching cropping: Crop the overlapping areas of Sentinel-1, GF-3 SAR, and SDGSAT-1 TIS preprocessed data to form sample pairs of Sentinel-1 and GF-3, Sentinel-1 and Sentinel-1, and Sentinel-1 and SDGSAT-1 with the same coverage range within a certain time interval.\n
3. Image classification: Use object-oriented and threshold segmentation methods to classify the ice water images in these areas, assign water value to 0 and ice value to 1, generate 10 km × 10 km grid binary classification sample pairs of ice water, and obtain the final dataset.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "海冰动力学", "冰水分类" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "海冰边缘区" ], "ds_time_tags": [ 2021, 2022, 2023 ], "ds_contributors": [ { "true_name": "黄琳", "email": "huanglin@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "邱玉宝", "email": "qiuyb@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "李洋", "email": "liyang216@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "余淑文", "email": "yushuwen23@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "钟万洋", "email": "3285417260@qq.com", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "黄琳", "email": "huanglin@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "邱玉宝", "email": "qiuyb@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "李洋", "email": "liyang216@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "余淑文", "email": "yushuwen23@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "钟万洋", "email": "3285417260@qq.com", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "邱玉宝", "email": "qiuyb@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "category": "其他" }