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Satellite remote sensing presents a cost-effective solution for synopticflood monitoring, and satellite-derived flood maps provide a computationallyefficient alternative to numerical flood inundation models traditionally used.While satellites do offer timely inundation information when they happen tocover an ongoing flood event, they are limited by their spatiotemporalresolution in terms of their ability to dynamically monitor flood evolution atvarious scales. Constantly improving access to new satellite data sources aswell as big data processing capabilities has unlocked an unprecedented numberof possibilities in terms of data-driven solutions to this problem.Specifically, the fusion of data from satellites, such as the CopernicusSentinels, which have high spatial and low temporal resolution, with data fromNASA SMAP and GPM missions, which have low spatial but high temporalresolutions could yield high-resolution flood inundation at a daily scale. Herea Convolutional-Neural-Network is trained using flood inundation maps derivedfrom Sentinel-1 Synthetic Aperture Radar and various hydrological,topographical, and land-use based predictors for the first time, to predicthigh-resolution probabilistic maps of flood inundation. The performance of UNetand SegNet model architectures for this task is evaluated, using flood masksderived from Sentinel-1 and Sentinel-2, separately with 95 percent-confidenceintervals. The Area under the Curve (AUC) of the Precision Recall Curve(PR-AUC) is used as the main evaluation metric, due to the inherentlyimbalanced nature of classes in a binary flood mapping problem, with the bestmodel delivering a PR-AUC of 0.85.

Towards Daily High-resolution Inundation Observations using Deep Learning and EO