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Signal-to-noise ratio for MTCI and NDVI time series data

Jadunandan Dash(1), Thomas Lankester(2), Steven Hubbard(2) and Paul Curran(3)

(1) University of Southampton, School of Geography, Southampton SO17 1BJ, United Kingdom
(2) Infoterra Ltd, Europa House, The Crescent, Farnborough GU14 0NL, United Kingdom
(3) Bournemouth University, Office of the Vice-Chancellor,Talbot Campus, Poole BH12 5BB, United Kingdom

Abstract

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.

 

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry