## 2 Data and Methods
### NDVI Datasets  
There are seven datasets being used in this study that utilize at least one of the three sensors described above. The characteristics of these datasets are also described in Table 1. These datasets are the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group 1 st Generation NDVI (NDVIg) [49], the 3 rd Generation GIMMS NDVI (NDVI3g) (improved version of [49]), the NOAA STAR-NESDIS smoothed NDVI (referred herein as STAR) [50], the SPOT VEGETATION NDVI VGT-S10 (referred to here as SPOT) [47], two datasets from MODIS 16-day 1 km (MOD13A2 and MYD13A2) NDVI data from the Terra and Aqua satellites respectively [48], and the Vegetation Index and Phenology (VIP) Version 2 global 15-day composite NDVI dataset [51,52]. Aqua was only used to test against Terra but due to its limited time overlap with other products and its very similar behavior compared to Terra, detailed analysis and discussion of Aqua is not included. It should be noted that for MODIS Collection 5, the blue band has been degrading on the Terra satellite and has resulted in a negative NDVI trend bias [53]. While, Terra has not been used in this study for trend analysis, the comparison results between Terra and the other products may be different than the relationship between Aqua and these products.  
Three datasets (NDVIg, NDVI3g, STAR) use AVHRR data to compute NDVI, Terra and Aqua use MODIS and SPOT uses the VEGETATION sensor. VIP uses all three sensors to create a synthesis NDVI product; however, it mostly relies on AVHRR and MODIS. Four products (NDVIg, NDVI3g, STAR, VIP) are considered long-term NDVI records because each dataset uses data from AVHRR beginning in 1981. The other three datasets have been available for the past decade and provide a comparison between datasets based on newer and more reliable sensors and the long-term datasets based mostly on AVHRR. For the VIP dataset, poor quality data has been removed based on an  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Sensor** & **Temporal Span** & **Temporal Resolution** & **Spatial Resolution** & **Compositing** \\ \hline NDVIg & AVHRR & 1981–2008 & bi-monthly & 8 km & MVC \\ NDVI3g & AVHRR & 1981–2011 & bi-monthly & 1/12 deg & MVC \\ STAR & AVHRR & 1981–2011 & weekly & 16 km & smoothed \\  & AVHRR & & & & \\ VIP & VEGETATION & 1981–2010 & 15-day & 0.05 deg & MVC \\  & MODIS & & & & \\ Terra & MODIS & 2000–2011 & 16-day & 1 km & CV-MVC \\ SPOT & VEGETATION & 1998–2011 & 10-day & 1 km & MVC \\ \hline \end{tabular}
\end{table}
Table 1: Description of Normalized Difference Vegetation Index (NDVI) datasets used in this study. The Aqua (2002–2011) dataset is very similar to the Terra dataset and hence is not included here.  
extensive cloud cover and aerosol analysis that considers annual, seasonal and daily cloud patterns evaluated for each individual pixel, different land cover classes and latitudinal bands. To provide continuity between AVHRR and MODIS data, quadratic equations were applied for AVHRR data separately for each NOAA satellite (Version 1) and linear regression equations were applied per vegetation class (Version 2; used in this study). Finally, an inverse-distance weighting scheme was used for filling in spatial gaps [52].  
The maximum-value composite (MVC) method [54] has been used to generate most of the NDVI datasets used in this study. This method reduces the influence on NDVI from clouds, spectral properties, resolution, and residual atmospheric effects that all act to reduce NDVI. Furthermore, this method often results in the same day being chosen for each pixel for different sensors [37]. However, this method does have a limitation in favoring high solar zenith view angles that may cause a high bias in some instances [55]. MODIS-based datasets use a modified constrained view MVC to correct for this effect.