Abstract
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.
Highlights
EXTREME rainfall-driven flooding is one of the most widespread and costly natural disasters [1] and is expected to become more frequent with global warming [2]
The Soil Moisture Active Passive (SMAP) L-band microwave radiometer is optimal for flood mapping from cyclone events characterized by heavy cloud cover and intense precipitation
The surface water inundation was depicted by SMAP fractional water cover (FW) observations for March 17-19, 2019 (Fig.3a), when extensive inundated areas were identified in the southeast African countries including Mozambique, Zimbabwe, Malawi and Madagascar [59]
Summary
EXTREME rainfall-driven flooding is one of the most widespread and costly natural disasters [1] and is expected to become more frequent with global warming [2]. Advances in remote sensing and big data techniques provide new opportunities for building efficient and effective all-weather and multi-scale flood assessment and forecast capabilities. Satellite optical-infrared (IR) and microwave remote sensing observations are suitable for delineating flood inundation extent over large areas due to the unique surface reflectance and microwave signatures of standing water [4]. Cloud cover and sub-optimal solar illumination can severely reduce the number of valid measurements from optical-IR remote sensing, resulting in major data loss during rainfall driven flood events [8]. Despite the drawbacks likely limiting near-real time flood monitoring, long-term water inundation records composited from clear-sky optical-IR observations are valuable in quantifying historical water inundation dynamics and flood feasibility [9,10]
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