Nitrogen prediction in grasses: effect of bandwidth and plant material state on absorption feature selection

Authors: Knox, N. M.1; Skidmore, A. K.1; Schlerf, M.1; de Boer, W. F.2; van Wieren, S. E.2; van der Waal, C.2; Prins, H. H. T.2; Slotow, R.3

Source: International Journal of Remote Sensing, Volume 31, Number 3, 2010 , pp. 691-704(14)

Publisher: Taylor and Francis Ltd

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We analysed stability and predictive capabilities of known nitrogen absorption features between plant material prepared for NIRS (dried) and RS (fresh) studies. Grass spectra were taken of the plant canopy, and again after the grass sample was dried and ground. Models were derived using stepwise multiple linear regression (sMLR). Regression values (adj.r2) produced using the dried material were greater than those produced using canopy material. For dried material only wavebands from the SWIR region were selected. Wavebands selected by sMLR on canopy material were located in both the VNIR and SWIR regions. Using wavebands selected for dried material models produced low adj.r2 values when applied to canopy plant material; differences in adj.r2 values are smaller when wavebands selected in canopy material models are applied to dried material. Widening of nitrogen features produced higher adj.r2 values for both dried and canopy material. This work shows that obtaining models with high predictive capabilities for nitrogen concentration is possible, but waveband selection should not be limited to features identified by NIRS studies. To accommodate for variability in absorption features, and instrument errors, absorption features should be widened.

Document Type: Research Article


Affiliations: 1: International Institute for Geo-information Science and Earth Observation, Enschede, The Netherlands 2: Resource Ecology Group, Wageningen University, Wageningen, The Netherlands 3: Amarula Elephant Research Programme, Biological and Conservation Sciences, Westville Campus, University of KwaZulu-Natal, Durban, South Africa

Publication date: April 1, 2010

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