Comprehensive validation of the benefits of multi-sensor flood monitoring

Multi-sensor methodologies are gaining traction within flood monitoring research, grounded in the rationale that data fusion from diverse sources mitigates uncertainty and improves spatiotemporal coverage. However, these assumed benefits are rarely quantified.

This work aims to comprehensively compare the performances of multi-sensor and single-sensor approaches to understand to what extent increasing the number and variegate data source may improve the detection rate and temporal characterisation of flood events. A multi-sensor flood monitoring approach using AMSR2 and VIIRS data is assessed against each sensor individually and against standard benchmarks in EO-based flood detection (e.g., MODIS and Sentinel-1)  for major flood events in the Savannakhet Province of Laos.

The comparative analysis evaluates multiple metrics. First, detection comparison classifies events as captured by each considered approach, multi-sensor only, each individual sensor only, or missed by all, to directly quantify the improvement attributable to multi-sensor integration. The spatial agreement is assessed between the multi-sensor and single sensor approaches for jointly detected flood events. Additionally, the temporal component is characterized by an examination of the observation frequency, maximum observation gaps, and peak capture timing. Lastly, the various detection outcomes are related to event characteristics, including cloud cover persistence, flood magnitude, duration, and flood type, quantifying the conditions under which a multi-sensor approach performs optimally.