Evaluation of AVHRR PAL and GIMMS 10-day composite NDVI time series products using SPOT-4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index)10-day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT-4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT-4 vegetation (VGT) 10-day composite (S10) NDVI data. Three years of AVHRR PAL (1998-2000) and seven years of GIMMS (1998-2004) have been compared to 8 km resampled SPOT-4 VGT (1998-2004) data. The dynamic range of SPOT-4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT-4 VGT NDVI. Ortho-regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT-4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT-4 VGT compared to the PAL/SPOT-4 VGT. Analyses on decade data likewise show that a linear relation exists between Spot-4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot-4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot-4 VGT NDVI compared to AVHRR PAL versus Spot-4 VGT NDVI ( in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra- annual variations in the correlation between SPOT-4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT-4 VGT cloud-screening algorithm is insufficient, thereby suppressing the rainy season NDVI.