Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data

Remote Sensing

Volumn 7, Issue 12, 2015, Pages 16398-16421

  Ali, Iftikhar ^a,b   Greifeneder, Felix ^c   Stamenkovic, Jelena ^d   Neumann, Maxim ^e   Notarnicola, Claudia ^c  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b TEAGASC   (Ireland)

^c INSTITUTE FOR RENEWABLE ENERGY   (Italy)

^d EPFL   (Switzerland)

^e CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Artificial neural networks; Biomass; Biophysical parameters; Machine learning; Regression; Remote sensing; Retrieval algorithms; Soil moisture; SVM

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMASS; LEARNING SYSTEMS; MOISTURE; NEURAL NETWORKS; SOIL MOISTURE; SOIL SURVEYS; SOILS; SPACE OPTICS; SPACE PLATFORMS;

BIOPHYSICAL PARAMETERS; GROUND MEASUREMENTS; HIGH DIMENSIONALITY; MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; REGRESSION; RETRIEVAL ALGORITHMS; TEMPORAL AND SPATIAL;

REMOTE SENSING;

EID: 84962670222     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71215841     Document Type: Review

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