Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP

Huan Wu, John S. Kimball, Naijun Zhou, Lorenzo Alfieri, Lifeng Luo, Jinyang Du, Zhijun Huang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Improving flood modeling accuracy is crucial for real-time flood monitoring and early warning systems. Knowing the sources, patterns and driving factors of model uncertainty aids the development of more accurate flood predictions. This study investigates the consistency of two global flood inundation products, i.e., the Soil Moisture Active Passive (SMAP) satellite based fractional water (Fw) cover and the Global Flood Monitoring System (GFMS) modeled flood inundation. Using Pearson's correlation coefficient (r) as the indicator of the SMAP-GFMS model consistency, this research documents the spatial and temporal patterns of the correlations between the two flood products, and investigates factors affecting these relationships, including climate, land cover, hydrology and terrain distributions. Results reveal that globally, 64% locations have moderate to strong SMAP-GFMS correlation (r ≥ 0.4). Locations that are dry and have low biomass and high seasonal flood variability tend to have high correlation; for example, 47% locations with r ≥ 0.4 occur in tropical and arid climate zones, and 43% locations with r ≥ 0.4 are observed in Barren, Evergreen Broadleaf Forest, Grasslands, Open Shrubland and Savannahs. Also, larger rivers have higher correlation, and in each Strahler stream order there are 60% to 65% locations having r ≥ 0.4. Larger watersheds show higher SMAP-GFMS consistency in particular watersheds between 1000 and 40,000 km2. Regions with greater urban infrastructure tend to have lower correlation, while locations with lower elevations and relatively flat topography have higher SMAP-GFMS consistency. This study indicates that GFMS and SMAP provide complementary information on surface water storage variations influencing precipitation driven runoff and flooding, which may enable enhanced global flood predictions.

Original languageEnglish
Article number111360
JournalRemote Sensing of Environment
StatePublished - Nov 2019


  • Flood inundation
  • GFMS
  • GPM
  • Remote sensing
  • SMAP


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