  Satellite remote sensing presents a cost-effective solution for synoptic
flood monitoring, and satellite-derived flood maps provide a computationally
efficient alternative to numerical flood inundation models traditionally used.
While satellites do offer timely inundation information when they happen to
cover an ongoing flood event, they are limited by their spatiotemporal
resolution in terms of their ability to dynamically monitor flood evolution at
various scales. Constantly improving access to new satellite data sources as
well as big data processing capabilities has unlocked an unprecedented number
of possibilities in terms of data-driven solutions to this problem.
Specifically, the fusion of data from satellites, such as the Copernicus
Sentinels, which have high spatial and low temporal resolution, with data from
NASA SMAP and GPM missions, which have low spatial but high temporal
resolutions could yield high-resolution flood inundation at a daily scale. Here
a Convolutional-Neural-Network is trained using flood inundation maps derived
from Sentinel-1 Synthetic Aperture Radar and various hydrological,
topographical, and land-use based predictors for the first time, to predict
high-resolution probabilistic maps of flood inundation. The performance of UNet
and SegNet model architectures for this task is evaluated, using flood masks
derived from Sentinel-1 and Sentinel-2, separately with 95 percent-confidence
intervals. The Area under the Curve (AUC) of the Precision Recall Curve
(PR-AUC) is used as the main evaluation metric, due to the inherently
imbalanced nature of classes in a binary flood mapping problem, with the best
model delivering a PR-AUC of 0.85.
