Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat.
Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat.
复制标题
DOI：
10.1109/jstars.2021.3092340
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
Wood EF
中科院分区：
文献类型：
--
作者：
Du J;Kimball JS;Sheffield J;Pan M;Fisher CK;Beck HE;Wood EF
多波段卫星观测用于洪水监测和预报的能力和协同使用对于改进备灾和减灾至关重要。在这里，地表分数水覆盖（FW）检索来自土壤水分主动被动（SMAP）L波段（1.4 GHz）亮度温度被用于洪水评估在非洲东南部的热带气旋伊代事件。然后，我们专注于Pungwe流域的五个子流域，并在Google Earth Engine的支持下开发了一种基于机器学习的方法，用于每日（24小时）预测FW和30米洪水降尺度和绘图。分类和回归树模型的选择和训练使用检索来自SMAP和大地卫星，再加上从NOAA全球预报系统的降雨量预报。独立验证表明，FW预测随机选择的日期是高度相关的（R = 0.87）与Landsat观测。预测结果捕获洪水的时间动态从伊达事件和相关的30米降尺度的结果显示，淹没的空间模式与独立的卫星合成孔径雷达观测一致。数据驱动的方法提供了利用协同卫星观测和大数据分析进行洪水监测和预报的新能力，这对数据稀少的地区特别有价值。
The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.