A Framework to Assess Remote Sensing Algorithms for Satellite-Based Flood Index Insurance

Mitchell Thomas, Elizabeth Tellman, Daniel E. Osgood, Ben Devries, Akm Saiful Islam, Michael S. Steckler, Maxwell Goodman, Maruf Billah

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Remotely sensed data have the potential to monitor natural hazards and their consequences on socioeconomic systems. However, in much of the world, inadequate validation data of disaster damage make reliable use of satellite data difficult. We attempt to strengthen the use of satellite data for one application-flood index insurance-which has the potential to manage the largely uninsured losses from floods. Flood index insurance is a particularly challenging application of remote sensing due to floods' speed, unpredictability, and the significant data validation required. We propose a set of criteria for assessing remote sensing flood index insurance algorithm performance and provide a framework for remote sensing application validation in data-poor environments. Within these criteria, we assess several validation metrics-spatial accuracy compared to high-resolution PlanetScope imagery (F1), temporal consistency as compared to river water levels (Spearman's ρ), and correlation to government damage data (R2)-that measure index performance. With these criteria, we develop a Sentinel-1 flood inundation time series in Bangladesh at high spatial (10 m) and temporal (∼weekly) resolution and compare it to a previous Sentinel-1 algorithm and a Moderate Resolution Imaging Spectroradiometer (MODIS) time series used in flood index insurance. Results show that the adapted Sentinel-1 algorithm (F1avg = 0.925, ρavg = 0.752, R2 = 0.43) significantly outperforms previous Sentinel-1 and MODIS algorithms on the validation criteria. Beyond Bangladesh, our proposed validation criteria can be used to develop and validate better remote sensing products for index insurance and other flood applications in places with inadequate ground truth damage data.

Original languageEnglish (US)
Pages (from-to)2589-2604
Number of pages16
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume16
DOIs
StatePublished - 2023

Keywords

  • Bangladesh
  • Sentinel-1
  • flood damage
  • flood hazard
  • index insurance
  • radar remote sensing
  • remote sensing validation
  • risk transfer
  • synthetic aperture radar (SAR)
  • time series

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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