Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application

Water Resources Management

Volumn 27, Issue 8, 2013, Pages 3127-3144

  Srivastava, Prashant K ^a   Han, Dawei ^a   Ramirez, Miguel Rico ^a   Islam, Tanvir ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Artificial intelligence; Artificial neural network; Generalized linear models; Relevance vector machine; SMOS; Soil moisture; Soil moisture deficit; Support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; ATMOSPHERIC TEMPERATURE; CONTINUOUS TIME SYSTEMS; IMAGE RESOLUTION; LAND SURFACE TEMPERATURE; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD; NEURAL NETWORKS; RADIOMETERS; SATELLITES; SCHEDULING; SOIL SURVEYS; SUPPORT VECTOR MACHINES; SURFACE MEASUREMENT; SURFACE PROPERTIES; TIME SERIES; VECTORS;

ARTIFICIAL INTELLIGENCE TECHNIQUES; GENERALIZED LINEAR MODEL; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; PROBABILITY DISTRIBUTION MODEL; RELEVANCE VECTOR MACHINE; SMOS; SOIL MOISTURE AND OCEAN SALINITY (SMOS); SOIL MOISTURE DEFICITS;

SOIL MOISTURE;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; DOWNSCALING; HYDROLOGICAL MODELING; MODELING; MODIS; SATELLITE DATA; SMOS; SOIL MOISTURE; SPATIAL RESOLUTION; SURFACE TEMPERATURE;

EID: 84876952993     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-013-0337-9     Document Type: Article

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