TECHNISCHE UNIVERSITAT WIEN (TU WIEN) (AT)
Through its role in the global water-, carbon- and energy cycles, vegetation is a key control in land surface processes and land-atmosphere interactions. Vegetation is strongly affected by variability in climate drivers like temperature, radiation and water availability. Vegetation phenology, the timing of vegetation phases, is a sensitive indicator of terrestrial ecosystem response to climate change, and changes herein, e.g. lengthening of the growing season, can influence terrestrial carbon uptake and thus, depending on the net effect, either exacerbate or dampen global warming. The effect of moisture availability on vegetation dynamics is still debated. While some studies found no relation between precipitation and vegetation dynamics when using visible-infrared (VI) remote sensing (RS), others attributed reductions in vegetation water, productivity and carbon uptake to droughts. Thus, the effects of water availability on vegetation dynamics and the subsequent feedbacks are still not fully understood. Nevertheless, understanding these effects are essential since droughts are expected to become more frequent with global warming and demand of agricultural food production increases to ensure global food security.
To identify the processes involved in interactions between climate drivers and vegetation dynamics long-term high-resolution Earth Observation (EO) datasets are needed. Microwave RS, with the advantage that it’s not hindered by clouds, smoke or illumination, provides complementary information on vegetation compared to VI RS. Vegetation Optical Depth (VOD), which describes the attenuation of microwave radiance by vegetation, is sensitive to the water content in the above ground biomass. Global VOD datasets are available from active and passive microwave observations, and have been successfully used to study trends and inter-annual variability in vegetation. However, to date the use of microwave observations has always been a trade-off between coarse spatial and high temporal resolution. With the Copernicus Sentinel-1 series, for the first time high temporal and spatial resolution backscatter time series have become available. Studies have demonstrated the sensitivity of the VH/VV Cross Ratio (CR) to vegetation. Here I will optimally combine the Sentinel-1 CR with VOD retrieved from EEUMETSAT Metop ASCAT backscatter observations to develop a global 1 km VOD product. The novel high-resolution VOD will be evaluated using Leaf Area Index from Copernicus Global Land Service (CGLS), ESA’s SMOS VOD and VOD from AMSR2. Subsequently, I will use novel machine learning approaches to quantify the impact of water availability on vegetation dynamics. The high-resolution VOD will allow the analysis of variations in impact of water availability on vegetation dynamics between land cover types, e.g. differences between natural and agricultural lands.
Remote Sensing of Environment