A Long-term VOD dataset is evaluated against NDVI and in situ biomass observations.
Both VOD and NDVI reflect the spatio-temporal patterns of biomass in West Sahel.
VOD captures variations of woody plant foliage biomass better than NDVI.
VOD and NDVI seasonal metrics differ for optimal long-term monitoring of biomass.
Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages.
In this study, we assess the capability of a long-term VOD dataset (1992–2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel.
Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r2 = 0.90).
Overall, in spite of the coarse resolution of 25 km, the study shows that VOD is an efficient proxy for estimating biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas.
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:
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:
a rough overlay highlights the different directions of annual trends:
The problem can be solved by excluding the dry season and using the small integral, calculated in TIMESAT:
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.