Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation

Ming Pan*, Eric F. Wood, Rafał Wójcik, Matthew F. McCabe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

113 Scopus citations


An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements.

Original languageEnglish (US)
Pages (from-to)1282-1294
Number of pages13
JournalRemote Sensing of Environment
Issue number4
StatePublished - Apr 15 2008
Externally publishedYes


  • Copula
  • Data assimilation
  • Ensemble Kalman filter
  • Particle filter
  • Remote sensing
  • SEBS
  • TRMM

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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