Satellite Video Remote Sensing for Flood Model Validation

Satellite-based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video-derived flood extents and velocities. Hydraulic simulations of a flood event with a 5-year return period (discharge of 722 m3 s-1) were conducted using Hydrologic Engineering Center-River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer-encoder, convolutional neural network (CNN)-decoder deep neural network. We evaluate the influence of test-time augmentation (TTA)-the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non-contact-based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network-based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space-based video sensors for validating flood models and studying flood dynamics. Videos of the Earth surface recorded by satellites can enable us to observe and characterize dynamic moving features, such as floods, that would otherwise be very difficult or dangerous to investigate from the ground. Hydrologists often rely on using physics-based computer models to simulate flood events, but require observational data to make sure these reflect reality accurately. We use artificial intelligence techniques to automatically detect flood extents from satellite video, and track surface features from frame to frame in order to measure how fast the water surface is flowing. Satellite video was collected during opportunistically clear skies in January 2022, along a 6.5 km length of the River Darling in Australia. The flood extent and flow velocities were used to improve numerical model predictions of the flood event. Our findings demonstrate the considerable promise of satellite video to complement existing flood mapping and modeling approaches, and to provide insight into the earth's hydrosphere, particularly in remote locations and during extreme conditions. Satellite video derived flood extents and velocities successfully validate 2D hydraulic model predictionsTest-time augmentation during deep learning inference improved flood extent delineation and enhanced 2D model validation metricsIncorporating characterization of discharge uncertainty into hydraulic model predictions resulted in more accurate model validation