Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350-2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass Cheyenne (Sorghastrum nutans (L.) Nash) and 25 % big bluestem Kaw (Andropogon gerardii Vitman)] and switchgrass Alamo} and high biomass sorghum Blade 5200 (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710-740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r(2) = 0.30 and RMSE = 1.6 Mg ha(-1) in 2012 and r(2) = 0.37 and RMSE = 4.0 Mg ha(-1), but was strongly correlated for the high biomass sorghum in 2013 (r(2) = 0.72 and RMSE = 4.6 Mg ha(-1)). Compared to the best narrowband VI, the RMSE of the PLSR model was 19-41 % lower for estimating N concentration and 4.2-100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.