AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series-A TIMELINE Thematic Processor

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981-2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.

Automated summary

Remote sensing image composites are crucial for various remote sensing applications, but there is no standardized procedure for creating NDVI time series over the AVHRR time-span. This study thoroughly assesses the effect of different compositing procedures on AVHRR NDVI composites and identifies the median approach as the best-performing algorithm. The generated NDVI time series shows consistent behavior and close agreement to the standard MODIS NDVI product.