Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model

Water Resources Management

Volumn 27, Issue 15, 2013, Pages 5069-5087

  Srivastava, Prashant K ^a   Han, Dawei ^a   Rico Ramirez, Miguel A ^a   Al Shrafany, Deleen ^a   Islam, Tanvir ^a,b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

ANN; Data fusion; Kalman filter; Linear weighted algorithm; Probability Distributed Model; SMOS; Soil moisture deficit; WRF NOAH LSM

Indexed keywords

ANN; DISTRIBUTED MODELING; SMOS; SOIL MOISTURE DEFICITS; WEIGHTED ALGORITHM; WRF-NOAH LSM;

ALGORITHMS; DATA FUSION; KALMAN FILTERS; LINEAR REGRESSION; NEURAL NETWORKS; PROBABILITY DISTRIBUTIONS; SATELLITES;

SOIL MOISTURE;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CALIBRATION; KALMAN FILTER; LAND SURFACE; NUMERICAL MODEL; PROBABILITY; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; SOIL MOISTURE;

EID: 84890144866     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-013-0452-7     Document Type: Article

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