Large-scale surface water change observed by Sentinel-2 during the 2018 drought in Germany

Abstract

Monitoring and understanding the spatio-temporal dynamics of hydrological droughts with seamless geographical coverage over large areas is essential for an assessment of impacts on water resources, industry, transport, and human health. This became parti- cularly relevant during the heat and drought of 2018 in Germany, which affected the country across various sectors and caused sig- nificant interruptions to ship traffic on rivers and lakes with negative impacts on tourism, transportation, and supply chains. In this study, we provide a spatially and temporally consistent view on the 2018 hydrological drought in Germany as seen from Sentinel-2 satellite images. We extract waterbodies with national coverage at different timestamps using an automated processing chain, which is based on a convolutional neural network and has originally been developed for near-real time flood monitoring. The method produces water segmentations with consistently high Overall Accuracy (≥0.95) and Kappa Coefficient (≥0.89), despite varying topography, land-use /land-cover and atmospheric conditions. Furthermore, we identify hotspots of change in water extent at a national scale by comparing monthly water maps for 2018 with the respective maps of the previous year 2017. For the change hotspots, we map change gra- dients and produce water extent time-series with mapping frequen- cies <5 days along a timeline of 12 months.