Abstract
During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series.
Highlights
South Africa has faced one of the most severe meteorological droughts during the southern summer season of 2015 and 2016, due to an exceptional El Niño event, which was ranked as third strongest since climate recordings [1,2]
This study focused on the extraction of surface moisture information from high spatial resolution Sentinel-1 time series between 2015 and 2017, using a change detection technique for drought monitoring in combination with normalized difference vegetation index (NDVI)-derived vegetation characteristics from Sentinel-2 and Landsat-8 for a test site in the savanna ecosystem of the southern part of the Kruger National Park
Multi-temporal Sentinel-2 and Landsat-8 NDVI information was derived to compare the findings from the SAR-based surface moisture index (SurfMI) to different vegetation cover types and their phenological dynamics
Summary
South Africa has faced one of the most severe meteorological droughts during the southern summer season of 2015 and 2016, due to an exceptional El Niño event, which was ranked as third strongest since climate recordings [1,2]. The El Niño/Southern Oscillation phenomenon (ENSO), which is driven by fluctuations of ocean temperatures in the equatorial Pacific [3], is negatively correlated with the amount of rainfall during the summer season in southern Africa [4,5]. A strong ENSO phenomenon has enormous impacts on ecosystem dynamics, as well as agricultural and biomass productivity in Remote Sens. Additional parameters derived from microwave remote sensing data, surface moisture in particular, are essential in the analysis and monitoring of impacts and dynamics of droughts in various ecosystems [20]. The retrieval of surface moisture information for analyzing the impacts of droughts is of high importance, as it is highly correlated to vegetation and soil respiration, which represents both root and microbial respiration, and is one of the main fluxes of carbon in savanna ecosystems [27]
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