Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 82, Issue , 2013, Pages 27-40

  Ramoelo, A ^a,b   Skidmore, A K ^b   Cho, M A ^a   Mathieu, R ^a   Heitkonig I M A ^c   Dudeni Tlhone, N ^a   Schlerf, M ^b,d   Prins, H H T ^c  

^a DSI CSIR Nanotechnology Innovation Centre   (South Africa)

^b UNIVERSITY OF TWENTE   (Netherlands)

^c WAGENINGEN UNIVERSITY   (Netherlands)

^d Public Research Centre   (Luxembourg)

Author keywords

In situ hyperspectral remote sensing

Indexed keywords

AGRICULTURE; MEAN SQUARE ERROR; NITROGEN; PHOSPHORUS; REMOTE SENSING; VEGETATION;

ENVIRONMENTAL VARIABLES; HYPERSPECTRAL REMOTE SENSING; INTEGRATED MODELING APPROACHES; NORMALIZED DIFFERENCE VEGETATION INDEX; PARTIAL LEAST SQUARE REGRESSION; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); ROOT MEAN SQUARE ERRORS; SPECTRAL ABSORPTION FEATURES;

ESTIMATION;

ACCURACY ASSESSMENT; CONCENTRATION (COMPOSITION); ESTIMATION METHOD; GRASS; LIVESTOCK; NDVI; NITROGEN; NONLINEARITY; PHOSPHORUS; RANGELAND; REMOTE SENSING; SAVANNA; SOIL NUTRIENT; SPECTRAL ANALYSIS; WILD POPULATION;

KRUGER NATIONAL PARK; SOUTH AFRICA;

EID: 84878740061     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2013.04.012     Document Type: Article

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