Be cautious using GIMMS3g v0 for trend analysis!

GIMMS3g NDVI is widely used to assess vegetation trends from local to global scale. And even though it is the best long term dataset available right now (July 2015), people should be aware that a serious error affects the dataset in semi arid areas, like the Sahel, which has severe impacts on trend analysis. The dry season values around 2004 suddenly drop, which is not happening in reality. It is definitely related to the sensor change from NOAA16 to NOAA17, and also the last change to NOAA18 has an impact on the time series. The good thing is that the wet season values should be usable. The developers are aware about this problem and a new version is on it’s way. To illustrate the issue, find attached the GIMMS3g NDVI (v0) averaged over the Sahel belt, note the drop at the sensor change (first blue line) and the recovery at the next sensor change:

gimms

To further illustrate how serious this problem affects trend analysis, the same area is shown in the VOD dataset, which is found to be reliable:vod

a rough overlay highlights the different directions of annual trends:

gimms_vod

The problem can be solved by excluding the dry season and using the small integral, calculated in TIMESAT:

sintgim_vo

see further literature:

  • Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., Wang, Y., 2015. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment 163, 326–340. doi:10.1016/j.rse.2015.03.031
  • Horion, S., Fensholt, R., Tagesson, T., Ehammer, A., 2014. Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel. International Journal of Remote Sensing 35, 2493–2515. doi:10.1080/01431161.2014.883104
  • Pinzon, J.E., Tucker, C.J., 2014. A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series. Remote Sensing 6, 6929–6960. doi:10.3390/rs6086929
  • Liu, Y.Y., van Dijk, A.I.J.M., McCabe, M.F., Evans, J.P., de Jeu, R.A.M., 2013. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Global Ecology and Biogeography 22, 692–705. doi:10.1111/geb.12024
  • Jonsson, P., Eklundh, L., 2004. TIMESAT–a program for analyzing time-series of satellite sensor data* 1. Computers & Geosciences 30, 833–845.
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