ESTIMATION OF MAIZE NITRATE CONCENTRATIONS USING EO-1 DATA AND A NON-LINEAR REGRESSION MODEL
- AQCCI: Unit for Env. Sc. and Management, School of Geo-and Spatial Sciences, Faculty of Natural and Agricultural Sciences, North – West University, South Africa
Keywords: maize, waveband selection, image spectroscopy, cforest, spectral ratios, nitrate assessments, random forest
Abstract. Nitrogen compounds such as nitrates are considered the most important limiting factor for crop productivity. Monitoring the status of this element in crops has moved from destructive to non-destructive approaches. Remote sensing with ever evolving technologies has taken the lead on different crops across the world. This study assessed the potential of EO-1 data (Hyperion) to estimate nitrate concentrations in maize (Zea mays) leaves. The image was captured over the study area after the 11th week of planting. The random forest algorithm was useful for band selection to reduce data redundancy in the imagery, and regression analysis for nitrate predictions. Maize nitrate concentrations were detectable with key contributing wavebands as 752, 1043, 681, 851, 1820, 762, 862, 640, 1850, 609, 589, 569 and 650nm. From this list, a subset corresponding to previously identified bands was used to develop vegetation spectral ratios. There was improvement in accuracy of predictions from using: all selected wavebands, all developed ratios, and selected ratios as independent variables for the model with 752–681 contributing the most to an R2 = 0.90; and RMSEP = 0.15. Therefore, selected bands of Hyperion to develop ratios could be used to monitor spatial variation of nitrate concentrations in maize from canopy level.