Multi-source hydrological soil moisture state estimation using data fusion optimisation

Lu Zhuo, Dawei Han

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
294 Downloads (Pure)

Abstract

Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H-V polarizations). The simple yet effective Local Linear Regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well- proven feature selection algorithm Gamma Test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model’s soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally the final daily-available hydrological soil moisture product significantly increases the Nash-Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.
Original languageEnglish
Pages (from-to)3267-3285
Number of pages19
JournalHydrology and Earth System Sciences
Volume21
Issue number7
Early online date4 Jul 2017
DOIs
Publication statusPublished - Jul 2017

Keywords

  • Hydrological soil moisture state (SMD)
  • Local linear regression (LLR)
  • Gama test
  • Soil Moisture and Ocean Salinity (SMOS)
  • Multi-angle brightness temperatures
  • North American Land Data Assimilation System 2 (NLDAS-2)
  • Moderate Resolution Imaging 29 Spectroradiometre (MODIS)
  • Land surface temperature

Fingerprint Dive into the research topics of 'Multi-source hydrological soil moisture state estimation using data fusion optimisation'. Together they form a unique fingerprint.

Cite this