Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

Remote Sensing of Environment

Volumn 118, Issue , 2012, Pages 127-139

  Verrelst, Jochem ^a   Muñoz, Jordi ^a   Alonso, Luis ^a   Delegido, Jesús ^a   Rivera, Juan Pablo ^a   Camps Valls, Gustavo ^a   Moreno, José ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Biophysical parameter retrieval; Gaussian Processes regression (GPR); Kernel ridge regression (KRR); Machine learning; Regression algorithms; Sentinel 2; Sentinel 3; Support vector regression (SVR)

Indexed keywords

BIOPHYSICAL PARAMETERS; GAUSSIAN PROCESSES REGRESSIONS; KERNEL RIDGE REGRESSION (KRR); MACHINE-LEARNING; REGRESSION ALGORITHMS; SENTINEL-2; SENTINEL-3; SUPPORT VECTOR REGRESSION (SVR);

BIOPHYSICS; CHLOROPHYLL; ESTIMATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GEODETIC SATELLITES; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; LEARNING SYSTEMS; MONITORING; NEURAL NETWORKS; REGRESSION ANALYSIS; VEGETATION;

PARAMETER ESTIMATION;

ACCURACY ASSESSMENT; ALGORITHM; CHLOROPHYLL; ENVISAT-1; GAUSSIAN METHOD; LANDSAT; LEAF AREA INDEX; MERIS; MODEL VALIDATION; MONITORING; REAL TIME; REGRESSION ANALYSIS; SATELLITE DATA; SATELLITE IMAGERY; SPATIAL RESOLUTION; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE; VEGETATION COVER;

EID: 83455181750     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2011.11.002     Document Type: Article

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