Abstract
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1/12° spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 × 40°), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well inter-calibrated before being used concurrently.
Highlights
Time series of remotely sensed normalized difference vegetation index (NDVI) are valuable data sets in various Earth science fields
For the purpose of our study, the extent of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set was cut to the extent of the study area (Figure 1), while preserving the original GIMMS NDVI3g grid with a spatial resolution of 1/12°
This holds for the multi-annual mean and standard deviation (Figure 4). Together these findings demonstrate a generally relative good correspondence between the GIMMS and Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation densities
Summary
Time series of remotely sensed normalized difference vegetation index (NDVI) are valuable data sets in various Earth science fields. NDVI time series have been successfully used for environmental monitoring [1,2,3], for providing agricultural outlooks and yield predictions [4,5,6], and for modeling wildlife occurrence/movement and biodiversity [7]. Such time series are excellent and unique proxies for assessing possible climate change impacts across the globe [8,9] and for large scale drought monitoring [10]. Phenological events were included in the study as pheno-indicators provide unique information for assessing possible climatic impacts on vegetation growth and the bio-sphere [1,3,13,47,48,49]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
