Fig. 2: The benefits of multi-sensor combination for flood detection.
From: A multi-sensor approach for increased measurements of floods and their societal impacts from space
a Flood detection potential of individual sensors and gain of detection over time. The orange envelope is overshadowed by the green as prior to Sentinel-1 and 2, events captured by all 3 missions were, in principle, events captured by Landsat. b Efficacy of multi-sensor combination in flood detection. The flexibility for the user to choose imagery based on cloud cover is denoted by the yellow envelope, users with higher tolerance to cloud cover expect a fraction of events closer to the upper limit of the envelope, users that require nearly cloud free images expect values closer to the lower limit of the envelope. c Beira City, Mozambique where Tropical Cyclone Idai made landfall on March 14, 2019. Base layer credits to QGIS. d A near-real time, natural color Sentinel-2 image acquisition of the Beira City flooding. e, f The impact of cloud cover on image viability illustrated by comparison of near-real time image acquisitions between a Radar (Sentinel-1; E) and optical (Sentinel-2; F) platform. Satellite-captured floodwater is represented by cyan in both panels. Sentinel-2 image credits: Copernicus Sentinel data (2019), processed by ESA, CC BY-SA 3.0 IGO.
