随着全球气候变化的加剧和极端天气事件的频发，洪水灾害对人类社会和生态环境的威胁日益增加，严重威胁人民的生命财产安全。传统的地面观测手段在大范围洪水监测方面存在局限性，难以满足快速、高效的应急需求。本研究基于雷达卫星影像信息，引入双极化水体指数法（SDWI-OSTU）、支持向量机(SVM)和随机森林(RF)等多种方法，并利用分类三元搭配(CTC)的集成策略，对巢湖流域2016年5月~7月和2020年6月~8月典型日期的洪涝淹没范围进行识别和精度评估，洪涝淹没范围监测误差保持在10%以内。采用“算法名称+日期”的方式命名数据文件。", "ds_source": "
Sentinel-1卫星是欧洲航天局哥白尼计划（GMES）中的对地观测卫星，由两颗卫星组成，分别为Sentinel-1A和Sentinel-1B，载有C波段合成孔径雷达，可提供连续图像（白天、夜晚和各种天气）。Sentinel-1有4种条带扫描模式，其中IW模式的SAR图像特别设计用于获取陆地表面的图像，具有VV和VH两种极化模式。因此，研究选用IW模式下Level-1的地距多视影像（Ground Range Detected，GRD）数据产品，从欧空局官网（https://scihub.copernicus.eu/） 下载了巢湖流域2016年和2020年汛期流域性大洪水期间的遥感影像，空间分辨率为10m。", "ds_process_way": "
基于多时相雷达影像数据，开展了基于双极化水体指数法、支持向量机、随机森林方法的水体识别模拟，并采用三套水体识别结果基于分类三元搭配的平衡指标计算单元计算各自的平衡精度和集成权重，加权计算多模型水体识别结果三元组合，并基于检验样本集评价水体集成识别精度，将集成结果的像元数据转为矢量边界，得到流域洪涝淹没范围边界。", "ds_quality": "
对于雷达影像人工标注样本点，分类三元搭配方法在不同时间下的识别稳定性和精度更高，平均Accuracy达到0.969，Precision为0.965，能够更有效地整合单一模型的优势，洪水淹没范围识别中提供更加一致和准确的分类结果的。对于无人机影像人工标注样本点，分类三元搭配方法的Accuracy和Precision均达到了0.900以上，能够较准确地识别水体区域。对于水域面积识别，分类三元搭配模型在大多数情况下能够提供较为准确的水域面积识别结果，洪涝淹没范围监测误差保持在10%以内。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "巢湖流域", "ds_acq_lon_east": 117.58999999999999, "ds_acq_lat_south": 31.16, "ds_acq_lon_west": 117.11999999999999, "ds_acq_lat_north": 31.43, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3612228528, "ds_files_count": 37, "ds_format": "*.tif", "ds_space_res": "10m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "233f8aa7-8f70-4abd-b828-3a34507ed8e0.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "37eb642a-c117-47e4-a677-07ecffb4b8b7", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.55" ], "quality_level": 3, "publish_time": "2025-03-27 20:18:19", "last_updated": "2025-06-30 11:36:58", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6788.2025", "i18n": { "en": { "title": "A Flood Inundation Extent Dataset for the Chaohu Basin Based on Radar Satellite Imagery Recognition", "ds_format": "", "ds_source": "
The Sentinel-1 satellite, part of the European Space Agency's Copernicus Program (GMES), is an Earth observation satellite system consisting of two satellites: Sentinel-1A and Sentinel-1B. It is equipped with a C-band synthetic aperture radar (SAR) capable of providing continuous imagery (day, night, and in all weather conditions). Sentinel-1 offers four stripmap scanning modes, among which the IW (Interferometric Wide) mode is specifically designed to capture images of land surfaces, featuring VV and VH polarization modes. Therefore, this study utilizes Level-1 Ground Range Detected (GRD) data products in IW mode. Remote sensing imagery during the basin-wide floods of the Chaohu Basin in the flood seasons of 2016 and 2020 was downloaded from the ESA website (https://scihub.copernicus.eu/), with a spatial resolution of 10 meters.", "ds_quality": "
For radar imagery manually labeled sample points, the classification ternary combination method demonstrates higher stability and accuracy over different time periods, with an average accuracy of 0.969 and precision of 0.965. It effectively integrates the strengths of individual models, providing more consistent and accurate classification results for flood inundation mapping. For manually labeled sample points from UAV imagery, the classification ternary combination method achieves both Accuracy and Precision above 0.900, enabling relatively accurate identification of water body areas. Regarding water area identification, the classification ternary combination model provides accurate results in most cases, maintaining flood inundation monitoring errors within 10%.", "ds_ref_way": "", "ds_abstract": "
With the intensification of global climate change and the increasing frequency of extreme weather events, flood disasters pose an escalating threat to human society and the ecological environment, severely endangering people's lives and property. Traditional ground-based observation methods have limitations in large-scale flood monitoring and are unable to meet the fast and efficient emergency response needs. This study, based on radar satellite imagery, introduces several methods such as the Dual-Polarization Water Index (SDWI-OSTU), Support Vector Machine (SVM), and Random Forest (RF), and uses the Classification Ternary Combination (CTC) ensemble strategy to identify and evaluate the accuracy of flood inundation areas on typical dates during the flood seasons of May to July 2016 and June to August 2020 in the Chaohu Basin. The monitoring error of flood inundation areas is kept within 10%. Data files are named using the format \"Algorithm Name + Date\".
", "ds_time_res": "", "ds_acq_place": "Chao Lake Basin", "ds_space_res": "10m", "ds_projection": "", "ds_process_way": "Based on multi-temporal radar imagery data, water body identification simulations were conducted using the dual-polarization water body index method, support vector machines, and random forest methods. The three sets of water body identification results were evaluated using the balanced index calculation unit based on the classification ternary combination to calculate their respective balanced accuracy and integration weights. A weighted combination of the multi-model water body identification results was performed. The integrated water body identification accuracy was assessed using a validation sample set, and the pixel data of the integrated results were converted into vector boundaries to delineate the flood inundation boundary within the basin.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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, "ds_topic_tags": [ "雷达影像数据", "哨兵一号", "分类三元搭配", "双极化水体指数" ], "ds_subject_tags": [ "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "巢湖流域" ], "ds_time_tags": [ 2016, 2020 ], "ds_contributors": [ { "true_name": "李伶杰", "email": "ljli@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李伶杰", "email": "ljli@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "李伶杰", "email": "ljli@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" } ], "category": "其他" }