InundationExtent_Hurricane_Florence_UAVSAR_V2.0.zip

The 2018 Hurricane Florence produced heavy rainfall and subsequent record-setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high-resolution (about 5 meters) full-polarized L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition method and a Random Forest classifier. Validation of the established model with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. This data set contains inundation extent information of four UAVSAR flight lines (i.e., 13510, 31509, 32023 and 35303) with at least four days’ observations from Sep 18 to Sep 23, corresponding to the area covering the Neuse, Cape Fear, and Lumbee Rivers as well as their tributaries in Eastern North Carolina

2018年飓风佛罗伦斯（Hurricane Florence）在美国北卡罗来纳州引发强降雨及后续创纪录的河道洪水。美国国家航空航天局/喷气推进实验室（NASA/JPL）于9月18日至23日期间，采集了每日分辨率约5米的全极化L波段无人航空载具合成孔径雷达（Uninhabited Aerial Vehicle Synthetic Aperture Radar，UAVSAR）数据。本研究结合极化分解方法与随机森林（Random Forest）分类器，利用UAVSAR数据构建洪水淹没范围检测框架。通过整理得到的地面参考数据对所建模型开展验证，结果显示：将线性极化信息与极化分解、地形变量相结合，可显著提升淹没分类精度；仅使用线性极化时的卡帕统计量（Kappa statistic）为64.3%，引入多源信息后该指标提升至91.4%。本数据集包含4条UAVSAR飞行航线的淹没范围信息，航线编号分别为13510、31509、32023与35303；这些航线在9月18日至23日期间至少拥有4天的观测数据，覆盖范围涵盖北卡罗来纳州东部的纽斯河（Neuse River）、开普菲尔河（Cape Fear River）、隆比河（Lumbee River）及其支流区域。