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.

中文翻译：

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使用 SMAP 和 Landsat 进行卫星洪水淹没评估和预测

多源卫星观测在洪水监测和预报方面的能力和协同使用对于改进备灾和减灾至关重要。在此，从土壤水分主动被动 (SMAP) L 波段 (1.4 GHz) 亮度温度获得的地表水覆盖率 (FW) 反演被用于在飓风 Idai 事件期间对非洲东南部的洪水进行评估。然后，我们专注于 Pungwe 盆地的五个子汇水面积，并在 Google 地球引擎的支持下开发了一种基于机器学习的方法，用于 FW 和 30 米淹没降尺度和绘图的每日（24 小时）预测。分类和回归树模型是使用源自 SMAP 和 Landsat 的检索以及来自 NOAA 全球预报系统的降雨预测来选择和训练的。 R = 0.87) 与 Landsat 观测值。预测结果捕捉到了 Idai 事件的洪水时间动态；相关的 30 米缩小结果显示了与独立卫星合成孔径雷达观测一致的淹没空间模式。数据驱动的方法利用协同卫星观测和大数据分析为洪水监测和预报提供了新的能力，这对于数据稀疏地区尤其有价值。