Estimating biomass on CRP pastureland: A comparison of remote sensing techniques

Biomass from land enrolled in the Conservation Reserve Program (CRP) is being considered as a biofuel feedstock source. A quick, accurate and nondestructive method to estimate biomass yield would be valuable for land managers to ensure sustainable production. The purpose of this study was to compare the ability of regression models to estimate biomass yields using data from satellite and ground based remote sensing platforms. Biomass yields and plant spectral responses were obtained at different phenological stages over two growing seasons (2011–2012) on an 8.1 ha CRP pasture in central Montana. Regression models were constructed using the normalized difference vegetation index (NDVI) and various band combinations from a hand held Crop Circle sensor and from Landsat satellite images. All models showed reasonable accuracy in estimating biomass, with a difference of <276 kg ha−1 or 8% of measured values. None of the models showed statistically significant differences (p > 0.05) between actual and estimated biomass. Results suggest that the usefulness of the spectral regions is a function of phenological growth stage. Red, red edge, and the near-infrared bands are more responsive at boot and peak growth stages while bands in the short-wave infrared increased the accuracy for the dormant stage biomass estimations. Land managers may construct spectral models to more effectively manage biomass resources.