Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat

Jinyang Du, John S. Kimball, Justin Sheffield, Ming Pan, Colby K. Fisher, Hylke E. Beck, Eric F. Wood

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.

Original languageEnglish
Article number9465655
Pages (from-to)6707-6715
Number of pages9
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
StatePublished - 2021


Manuscript received December 17, 2020; revised May 18, 2021; accepted June 17, 2021. Date of publication June 25, 2021; date of current version July 14, 2021. This work was conducted at the University of Montana with funding from NASA under Grant NNX14AI50G and Grant NNX15AB59G. (Corresponding author: Jinyang Du.) Jinyang Du and John S. Kimball are with the Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59801 USA (e-mail: [email protected]; [email protected]).

FundersFunder number
National Aeronautics and Space AdministrationNNX15AB59G, NNX14AI50G


    • Flood
    • Global Forecast System (GFS)
    • Google Earth Engine (GEE)
    • Landsat
    • Soil Moisture Active Passive (SMAP)


    Dive into the research topics of 'Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat'. Together they form a unique fingerprint.

    Cite this