## 4 Conclusions  
In this study we have intercompared four long-term NDVI datasets and three shorter-term NDVI datasets based on newer sensors. The correlation between NDVI products based on the two new sensors (MODIS and SPOT) is higher than that between AVHRR-based products or between AVHRR- and MODIS/SPOT-based products. Most regression slopes are close to 1 among all products; however, STAR is consistently lower than all other NDVI datasets. The spatial variation in temporal correlation shows that most areas are well correlated; however, lower correlations occur in arid and mountainous regions. There are also large spatial variations in regression slopes between various products but the patterns of these variations depend on the specific products being compared. Temporal variation in the spatial correlation of NDVI shows high correlations above 0.8 for the entire time period with the lowest correlations between STAR and other datasets. Long-term datasets show significant changes in regression slopes through time especially between VIP and other datasets due to VIP's changing dependence from AVHRR before 2000 to MODIS afterwards. With respect to land cover classes, there are significant inter-product differences in mean \(N_{p,max}\) but interannual variation in \(N_{p,max}\) is fairly low (0.02 to 0.04) for most vegetation classes with the exception of more sparsely vegetated shrublands, grasslands and barren areas. Trend analyses of \(N_{p,max}\) and NDVI-based phenology for the four long-term datasets suggest that trends in \(N_{p,max}\) from NDVI3g are positive for most pixels with significant trends at the 95% confidence level. An increasing trend in duration of season mostly due to a delay in the end of season is apparent over much of the US east of the Rocky Mountains with the exception of agricultural areas such as the Corn Belt. Also, results suggest an earlier start of season over the Sierra Madre Occidental area of Mexico. Histograms of differences in NDVI phenology trends suggest VIP and STAR are characteristic of more positive trends in \(N_{p,max}\) and duration of season than NDVIIg and NDVI3g. However, there are still many pixels that show more negative trends as well, suggesting a lot of spatial variation in the difference of trends. In most temperate vegetation classes the trend is towards a delayed end of season across all products. A method of normalizing NDVI using GVF was tested to determine if the GVF-derived datasets are better related and produce more uniform trends than the original NDVI datasets. The results showedthat the change to GVF creates more one-to-one temporal and spatial relationships between products and long-term changes in these relationships are minimized compared to original NDVI relationships. Overall, there is a slight reduction in temporal correlation; however, spatial correlations increase between most products. When looking at different LC classes, MGVF is higher than \(N_{p,max}\), more uniform across different datasets and there is less interannual range. In general, the magnitude of MGVF changes are reduced compared to \(N_{p,max}\) changes east of the Rocky Mountains. Trends in both MGVF and GVF-based phenology are weaker and closer to each other across products than for NDVI. The results for trends in GVF-based phenology across LC classes are similar to NDVI-based trends but generally weaker with less agreement across datasets especially for MGVF trends. There are a few implications that result from this study. Due to the high inter-product correlation and better trend agreement, more consistent conclusions are expected for vegetation studies over deciduous forest areas; whereas, over evergreen, arid and mountainous regions results are less reliable.