Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the Sahel region

ABSTRACT

Over the last few decades, the African Sahel has become the focus of many studies regarding vegetation dynamics and their relationships with climate and people. This is because rainfall limits the production of biomass in the region, a resource on which people are directly dependent for their livelihoods. In this study, we utilized a remote-sensing approach to answering the following two questions: (1) how does the dynamic relationship between soil moisture and plant growth vary across hydrological regimes, and (2) are vegetation-type-dependent responses to soil moisture availability detectable from satellite imagery? In order to answer these questions, we studied the relationship between monthly modelled soil moisture as an indicator for water availability and the remotely sensed normalized difference vegetation index (NDVI) as a proxy for vegetation growth between a “recovery rainfall period” (1982 to 1997) and a “stable rainfall period” (1998 to 2013), at different time lags across the Sahel region. Using windowed cross-correlation, we find a strong significant positive relationship between NDVI and soil moisture at a concurrent time and at NDVI lagging behind soil moisture by 1 month for grassland, cropland, and deciduous shrubland vegetation – the dominant vegetation classes in the Sahel. South of the Sahel (the Sudanian and Guinean areas), we find longer optimal lags (soil moisture lagged by 1–3 months) in association with mixed forest and deciduous shrubland. We find no major significant change in optimal lag between the recovery and stable periods in the Sahelian region; however, in the Sudanian and Guinean areas, we observe a trend towards shorter time lags. This change in optimal lag suggests a vegetation change, which may be a response to a climatic shift or land-use change. This approach of identifying spatiotemporal trends in optimal lag correlations between modelled soil moisture and NDVI could prove to be a useful tool for mapping vegetation change and ecosystem behaviour, in turn helping inform climate change mitigation approaches and agricultural planning.

Acknowledgements

We acknowledge NASA Global Inventory Modelling and Mapping Studies (GIMMS) group for producing and sharing the AVHRR GIMMS NDVI3g data as well as NOAA NCEP Climate Prediction Center for providing the modelled soil moisture time series data and the NOAA/OAR/ESRL PSD for providing GPCP precipitation data. We also acknowledge the Erasmus Munds Programme for offering a scholarship to Mohamed Ahmed during his studies in Lund University in Sweden. Finally, the authors wish to thank the anonymous reviewers for their valuable comments and suggestions to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

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Additional information

Funding

This work was supported by the LUsTT: [Grant Number 211-2009-1682]; LUCID; Lund University; and FORMAS: [Grant Number 259-2008-1718].