Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing

Remote Sensing

Thomas Udelhoven¹  Philippe Delfosse²  Christian Bossung¹  Franz Ronellenfitsch²  Frຝéric Mayer²  Martin Schlerf²  Miriam Machwitz² 

[1] Department of Environmental Remote Sensing and Geoinformatics, Trier University, D-54286 Trier, Germany; E-Mail:;Department Environment and Agro-Biotechnologies, Centre de Recherche Public-Gabriel Lippmann, 41, rue du Brill, L-4422 Belvaux, Luxembourg; E-Mails:

关键词: agriculture;    bioenergy;    biomethane potential;    hyperspectral remote sensing;   

DOI  :  10.3390/rs5010254

来源: mdpi

【 摘 要 】

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMP_FM, Nm³ biogas/t fresh matter (Nm³/t FM)) and BMP per area (BMP_area, Nm³ biogas/ha (Nm³/ha)). We found that BMP_FM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (R_cv² = 0.82, n = 35), especially at levels >190 Nm³/t. In the in situ approach PLSR prediction quality declined (R_cv² = 0.50, n = 20). BMP_area, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.