Evaluating water controls on vegetation growth in the semi-arid sahel using field and earth observation data

Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105-2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO2 fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region

Global-scale mapping of changes in ecosystem functioning from earth observation-based trends in total and recurrent vegetation

Aim: To evaluate trend analysis of earth observation (EO) dense time series as a new way of describing and mapping changes in ecosystem functioning at regional to global scales. Spatio-temporal patterns of change covering 1982-2011 are discussed in the context of changes in land use and land cover (LULCC). Location: Global. Methods: This study takes advantage of the different phenological cycles of recurrent vegetation (herbaceous vegetation) and persistent vegetation (woody/shrub cover) in combining trend analyses of global-scale vegetation based on different annual/seasonal normalized difference vegetation index (NDVI) metrics. Spatial patterns of combined vegetation trends derived from the Global Inventory Modeling and Mapping Studies NDVI are analysed using land-cover information (GLC2000). Results: The direction of change in annual and seasonal NDVI metrics is similar for most global terrestrial ecosystems, but areas of diverging trends were also observed for certain regions across the globe. These areas are shown to be dominated by land-cover classes of deciduous forest in tropical/subtropical areas. Areas of observed change are found in dry deciduous forest in South America and central southern Africa and are in accordance with studies of hotspot LULCC areas conducted at local and regional scales. The results show that dense time series of EO data can be used to map large-scale changes in ecosystem functional type that are due to forest cover dynamics, including forest degradation, deforestation/reforestation and bush encroachment. Main conclusions: We show that areas characterized by changes in ecosystem functioning governed by LULCC at regional and global scales can be mapped from dense time series of global EO data. The patterns of diverging NDVI metric trends can be used as a reference in evaluating the impacts of environmental changes related to LULCC and the approach may be used to detect changes in ecosystem functioning over time