Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat.

Remote Sens Environ

Numerical Terradynamic Simulation Group, W.A. Franke College of Forestry & Conservation, The University of Montana, Missoula, MT 59812, United States.

Published: August 2018

AI Article Synopsis

  • Developed a method using L-band microwave observations from the SMAP mission to assess global land surface water inundation dynamics with high resolution and frequency.
  • The resulting data aligns well with existing water maps and captures seasonal inundation variations effectively.
  • Uncertainty analysis shows reliable performance in areas with low to moderate vegetation, while finer resolution results highlight accuracy in water and land classifications compared to Landsat imagery.

Article Abstract

A method to assess global land surface water () inundation dynamics was developed by exploiting the enhanced sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band ( ) retrievals were derived using SMAP H-polarization brightness temperature ( ) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency observations from AMSR2. The resulting global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The annual averages corresponded favourably (=0.85, <0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m retrievals showed favourable spatial accuracy for water (commission error 31.46%, omission error 30.20%) and land (commission error 0.87%, omission error 0.96%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6055934PMC
http://dx.doi.org/10.1016/j.rse.2018.04.054DOI Listing

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