Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

International Journal of Remote Sensing

Volumn 36, Issue 6, 2015, Pages 1743-1761

  Adjorlolo, Clement ^a,b   Mutanga, Onisimo ^b   Cho, Moses Azong ^b,c  

^a SOUTH AFRICAN NATIONAL SPACE AGENCY   (South Africa)

^b UNIVERSITY OF KWAZULU NATAL   (South Africa)

^c DSI CSIR Nanotechnology Innovation Centre   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

INFRARED RADIATION; LEAST SQUARES APPROXIMATIONS; MOISTURE; REFLECTION; REGRESSION ANALYSIS;

DATA DIMENSIONALITY; FIBRE CONCENTRATIONS; HYPERSPECTRAL REFLECTANCE; PARTIAL LEAST SQUARES REGRESSION; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); PREDICTION ACCURACY; RESAMPLING TECHNIQUE; VARIABLE IMPORTANCES;

NUTRIENTS;

C3 PLANT; C4 PLANT; FORAGE; GRASS; LEAST SQUARES METHOD; NUTRIENT CYCLING; REGRESSION ANALYSIS;

EID: 84926224628     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2015.1024893     Document Type: Article

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