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Open AccessArticle

Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing

1
Department of Environmental Remote Sensing and Geoinformatics, Trier University, D-54286 Trier, Germany
2
Department Environment and Agro-Biotechnologies, Centre de Recherche Public-Gabriel Lippmann, 41, rue du Brill, L-4422 Belvaux, Luxembourg
*
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(1), 254-273; https://doi.org/10.3390/rs5010254
Received: 20 November 2012 / Revised: 4 January 2013 / Accepted: 4 January 2013 / Published: 15 January 2013
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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 (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, 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. View Full-Text
Keywords: agriculture; bioenergy; biomethane potential; hyperspectral remote sensing agriculture; bioenergy; biomethane potential; hyperspectral remote sensing
MDPI and ACS Style

Udelhoven, T.; Delfosse, P.; Bossung, C.; Ronellenfitsch, F.; Mayer, F.; Schlerf, M.; Machwitz, M.; Hoffmann, L. Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing. Remote Sens. 2013, 5, 254-273.

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