Studying C functional group distributions in decomposing litterfall samples is one of the common methods of studying litterfall decomposition processes. However, the methods of studying the C functional group distributions, such as 13 C-NMR spectroscopy, are expensive and time consuming and new rapid and inexpensive technologies should be sought. Therefore, this study examined whether laboratory-based hyperspectral image analysis can be used to predict C functional group distributions in decomposing litterfall samples. Hyperspectral images were captured from ground decomposing litterfall samples in the visible to near infrared (VNIR) spectral range of 400-1000 nm. Partial least-square regression (PLSR) and artificial neural network (ANN) models were used to correlate the VNIR reflectance data measured from the litterfall samples with their C functional group distributions determined using 13 C-NMR spectroscopy. The results showed that alkyl-C, O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1, aryl-C2 and carboxyl derivatives could be acceptably predicted using the PLSR model,