Signal-to-noise Ratio for Mtci and Ndvi Time Series Data

The Phenology of vegetation varies with climate and variability in phenology is a powerful measure of climate change. Remotely-sensed data can be used to produce phenology curves that capture ‘green-up’, maturity and senescence from local to global scales. These curves are usually produced with Normalised Difference Vegetation Index (NDVI) data but are notoriously noisy. The MERIS Terrestrial Chlorophyll Index (MTCI) is related to the chlorophyll content, does not suffer from some of the limitations of NDVI (e.g., saturation at high biomass) and should, it was hypothesised, produce a less noisy phenological curve. Two methods were used to determine the phenological curve (signal) and Variability in the curve (noise); iterative polynomial fitting and discrete Fourier transformation. The signal-to-noise ratio (SNR) for MTCI curves was significantly higher than for the NDVI curves and this difference was largest for high green biomass areas. This was probably the result of the compositing techniques typically used for MTCI data. However, the two methods of SNR calculation produced different results for the NDVI but not the MTCI, thus suggesting that there was bias in the less noisy NDVI curve.