See more from this Session: Remote Sensing and Regional Scale Modeling
Wednesday, November 3, 2010: 12:45 PM
Long Beach Convention Center, Room 102A, First Floor
This study was conducted to evaluate the ability of using the combination between the Canopy Chlorophyll Content Index (CCCI) and the Canopy Nitrogen Index (CNI) for developing a unique approach to estimate canopy N status directly from remote measurements, independently of cultivar and site. The validation of the CCCI and its relationship with the CNI developed in a previous study might put us one step closer to a non-destructive way of assessing canopy N in real time. Therefore the aim of this study was to validate the algorithm developed for estimating canopy N concentration from CCCI in two rainfed environments and on two different wheat cultivars. Data were collected from two rainfed field sites cropped to wheat, one in Southern Italy (Foggia) and the other in the south eastern Australia (Horsham). On both sites were measured biomass, LAI, hyperspectral remote sensing, canopy nitrogen concentration and nitrogen content. Results indicate that while the estimation of CNI can be made with a predictive equation developed in a previous study and CCCI could be estimated with given coefficients, the relationship between CNI and CCCI did not hold. Therefore such relationship has to be estimated locally before remotely estimate canopy N. However, once such relationship is established, there was a good prediction of canopy N (g N m-2) (y=0.93x + 0.17; r2=0.96; Root Mean Square Error= 0.19 g N m-2; p<0.001; n=65).