An assessment of three candidate compositing methods for global MERIS time series

Compositing has long been used to produce complete, cloud-free images over large areas. Several compositing methods have been developed and implemented for global time series, and each method corrects for angular effects and atmospheric variations differently. This study assesses the performance of three compositing methods on real and simulated medium-resolution imaging spectrometer (MERIS) images. The three methods considered are best index slope extraction (BISE), mean compositing (MC), and the method of Hagolle et al. The optimal method was selected using two criteria, namely a qualitative examination of the temporal MERIS reduced resolution (RR) profiles, and a quantitative analysis of the noise introduced into composite images of the reflectance data time series. The latter calculation relies on the standard deviation of the normalized error based on simultaneous SPOT VEGETATION images. All three methods succeed in reproducing the long-term temporal evolution of the normalized difference vegetation index (NDVI). The BISE method, however, produces a temporal profile with more short-term variations. The other two methods produce very similar noise levels, with MC holding a small but consistent advantage. Owing to its performance and simplicity, the MC method has been selected to process global MERIS time series.