Estimation of soil moisture in mountain areas using SVR technique applied to multiscale active radar images at C-band

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 8, Issue 1, 2015, Pages 262-283

  Pasolli, Luca ^a   Notarnicola, Claudia ^b   Bertoldi, Giacomo ^b   Bruzzone, Lorenzo ^c   Remelgado, Ruben ^b   Greifeneder, Felix ^b   Niedrist, Georg ^b   Della Chiesa, Stefano ^b   Tappeiner, Ulrike ^b,d   Zebisch, Marc ^b  

^a Informatica Bancaria Trentina   (Italy)

^b INSTITUTE FOR RENEWABLE ENERGY   (Italy)

^c UNIVERSITY OF TRENTO   (Italy)

^d UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

Machine learning; SAR images; soil moisture

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GEODETIC SATELLITES; LANDFORMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MEAN SQUARE ERROR; MOISTURE; RADAR IMAGING; SOILS; SPACE-BASED RADAR; SYNTHETIC APERTURE RADAR;

EXPERIMENTAL ANALYSIS; GROUND MEASUREMENTS; METEOROLOGICAL STATION; MULTI SCALE ANALYSIS; ROOT MEAN SQUARED ERRORS; SAR IMAGES; SOIL MOISTURE DYNAMICS; SPATIAL HETEROGENEITY;

SOIL MOISTURE;

ALGORITHM; ENVISAT; MOUNTAIN REGION; RADAR IMAGERY; RADARSAT; SOIL MOISTURE; SPECKLE; SYNTHETIC APERTURE RADAR; TOPOGRAPHY; VEGETATION INDEX;

ALTO ADIGE; ITALY; TRENTINO-ALTO ADIGE;

EID: 85027946179     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2378795     Document Type: Article

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