Surface moisture and vegetation cover analysis for drought monitoring in the 
southern Krüger National Park using sentinel-1, sentinel-2, and landsat-8

Urban, Marcel; Berger, Christian; Mudau, Tami E.; Heckel, Kai; Truckenbrodt, John; Odipo, Victor Onyango; Smit, Izak P. J.; Schmullius, Christiane

doi:10.3390/rs10091482

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Surface moisture and vegetation cover analysis for drought monitoring in the southern Krüger National Park using sentinel-1, sentinel-2, and landsat-8

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Heckel, Kai
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

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http://dx.doi.org/10.3390/rs10091482
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Citation

Urban, M., Berger, C., Mudau, T. E., Heckel, K., Truckenbrodt, J., Odipo, V. O., et al. (2018). Surface moisture and vegetation cover analysis for drought monitoring in the southern Krüger National Park using sentinel-1, sentinel-2, and landsat-8. Remote Sensing, 10: 1482. doi:10.3390/rs10091482.

Cite as: https://hdl.handle.net/21.11116/0000-0002-1CBC-C

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.