A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy

Remote Sensing

Volumn 10, Issue 1, 2018, Pages

  Yue, Jibo ^a,b,c   Feng, Haikuan ^a,d   Yang, Guijun ^a,c,d   Li, Zhenhai ^a,c,d  

^a Ministry of Agriculture Key Laboratory of Resource Remote Sensing and Digital Agriculture   (China)

^b NANJING UNIVERSITY   (China)

^c NATIONAL ENGINEERING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

^d Beijing Engineering Research Center of Agricultural Internet of Things   (China)

Author keywords

Biomass; Biomass estimation model; Hyperspectral; Multi collinearity; Noise immunity; Regression techniques; Sampling methods; Vegetation indexes

Indexed keywords

BINARY TREES; BIOMASS; CROPS; DECISION TREES; ENERGY UTILIZATION; GAUSSIAN NOISE (ELECTRONIC); LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SOLAR ENERGY; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; VEGETATION;

BIOMASS ESTIMATION; COLLINEARITY; HYPERSPECTRAL; NOISE IMMUNITY; REGRESSION TECHNIQUES; SAMPLING METHOD; VEGETATION INDEX;

REGRESSION ANALYSIS;

EID: 85040688083     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10010066     Document Type: Article

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