## 1 Introduction  
Understanding ecosystem responses and feedbacks to a changing climate in a complex environment requires vegetation datasets that are consistent, accurate, and precise and directly represent real biophysical parameters of a vegetation ecosystem. Ecosystem changes are caused by a variety of factors both natural and anthropogenic including land use changes [1], invasive species [2], climate change [3,4] and increases in carbon dioxide [5]. Furthermore, dynamic vegetation models that simulate long-term vegetation growth require accurate vegetation datasets for validation in order to provide confidence in prognostic forecasts of vegetation change due to the factors listed above. The most accurate way to detect vegetation changes is through direct ground observations. However, detecting these changes across large areas would require a large number of research sites such as the Long Term Ecological Research sites over North America [6]. It is not feasible to scale data from isolated research sites up to the task of large-scale long-term continuous monitoring of vegetation activity. The most effective method to monitor vegetation across these scales is through satellite-based vegetation estimates. Currently, the most appropriate way to estimate long-term changes in vegetation via satellites is through the Advanced Very High Resolution Radiometer (AVHRR) which has a nearly continuous record since July 1981 until present. Unlike newer sensors, AVHRR has only two channels that are used for vegetation detection; the near-infrared (NIR) and red (R) bands. The chlorophyll in vegetation causes a decrease in canopy reflectance from the R band and leaves are generally highly reflective in the NIR band. These bands have been combined into spectral indices that more directly relate to vegetation characteristics such as the Normalized Difference Vegetation Index (NDVI) [7,8]. NDVI is the normalized ratio of NIR to R reflectance (\(N=(\rho_{NIR}-\rho_{R})/(\rho_{NIR}+\rho_{R})\)) and varies from \(-1\) to \(1\) with values close to one indicating high vegetation content and values close to zero indicating no vegetation over bare ground. Materials such as water which absorb more radiation in the NIR than visible actually have a negative NDVI. Research has shown strong relationships between NDVI and biophysical properties such as leaf area index (LAI) [9], fraction of photosynthetically active radiation (fPAR) [10], areal vegetation fraction [11, 12, 13, 14], net and gross primary productivity [15], chlorophyll abundance [16], and biomass [17]. It should be pointed out that areas not covered by vegetation may still show variations in NDVI due to atmospheric variations such as water vapor and aerosols, soil conditions as well as sensor characteristics [18]. NDVI has been used to study large-scale vegetation change at the global scale [19, 20, 21, 22, 23], Northern Hemisphere [24] as well as across the US and Mexico [25, 26, 27]. Furthermore, there have been several studies to explore large-scale interannual and seasonal dependence of satellite-based vegetation on climate [28, 29, 30, 31] as well as how vegetation feeds back to the atmosphere [26, 32]. It is important to compare the interannual variability and trends of various NDVI datasets to help in the development of consistent long-term multisensor NDVI records [33]. While different AVHRR-based records have been compared to each other [34, 35, 36] and some of these records have been compared with newer sensors such as the Satellite Pour l'Observation de la Terre SPOT-VGT [37, 38, 39, 40] and Moderate Resolution Imaging Spectroradiometer (MODIS) [23, 37, 39], new long-term NDVI records have been developed recently (NDVI3g, VIP, STAR) that should improve over previous generationNDVI datasets (NDVIg, PAL, FASIR) and there is now a need to compare these datasets to datasets from newer sensors such as MODIS and SPOT.