## 4 Conclusions
Also, because there are still significant disagreements between different NDVI datasets and the derived GVF datasets regarding seasonal and interannual variability, one should be cautious in drawing any conclusions regarding the long-term trend of vegetation coverage and phenology if only one satellite-based vegetation dataset is used. However, it's not always practical to use multiple datasets. For studies that use a single dataset, important trends should be checked against some other form of data such as in-situ or modelled vegetation data [64] to confirm hypotheses. The transformation of NDVI into GVF using the method described here created more homogeneous datasets that can each be compared to physical model variables and provide multiple instances of validation to a single model. Furthermore, they can be used to force vegetation changes in models that don't have dynamic vegetation. Finally, while some work has been done on creating regional [65] and global [51] multi-sensor NDVI synthesis products, in order to create datasets more capable of validating and calibrating dynamic vegetation models, the goal of the satellite vegetation community should be to create a vegetation dataset that estimates real vegetation quantities such as green vegetation fraction, vegetation health, leaf area index, fPAR and biomass, and identifies the uncertainty of all values for each time period and pixel once systematic errors have been removed. While the current study broadly compared several recent NDVI datasets and the usefulness of GVF in homogenizing them, a series of more targeted studies need to be undertaken to fully understand the differences that exist between various datasets. The most important limitation of this study is that there was no validation against data that could serve as a \"ground truth\". In order to make a value judgment on how well each product performs under various situations, future studies should validate datasets against _in-situ_ vegetation measurements, flux tower observations, and high resolution remote sensing including satellite images and field scale photography. Possible underlying reasons for dataset differences need to be explored, including different methods of correcting atmospheric effects such as water vapor, clouds and aerosols, different spatial and temporal compositing methods used, different spectral response functions of the sensors, and orbital and sensor degradation. Another limitation to this study was the simple threshold-based phenology detection. A more robust methodology is needed to track phenology changes in arid and evergreen areas which often have multiple growing seasons, limited seasonal vegetation range, and interannual variations in vegetation that can be stronger than seasonal variations. A more critical analysis of trends and the interannualvariability of datasets is needed. Are interannual trends linear or are these trends changing over time? Do datasets have similar modes of variability? Furthermore, the root cause of such variability needs to be determined separating out real vegetation variations from those due to systematic errors or uncertainties in the datasets. Variability of these datasets should be compared to ground truth as well as modelled vegetation variability driven by observed climate changes, fires, diseases and land-use change. A good understanding of the causes behind interannual variability will also help in determining the best method of computing the GVF and/or other biophysical variables based on satellite vegetation indices. In future studies the GVF should be based on \(N_{c,v}\) and \(N_{s}\) derived from histograms of \(N_{p,max}\) for all pixels across the globe for each land cover class rather than the regional data used here. Furthermore, experimental work is needed to determine the true relationship between GVF and NDVI, and how vegetation NDVI and bare soil NDVI vary in space and time, using data from the highest resolution to the coarser satellite data used in this study.