Open surface water classification based on multispectral sensors at moderate spatial resolution of VIIRS and OLCI data and harmonization of derived time series with MODIS based Global WaterPack (GWP) data

Kurzfassung

Climate change affects many aspects of human lives. Changing precipitation intensities combined with a warmer climate, as well as seasonal shifts and man-made changes in water regimes exert pressure on freshwater systems on a global scale. With agriculture as the main consumer, to date a significant portion of the world’s population already lives in areas characterized by water scarcity. The IPCC's most recent Sixth Assessment Report suggests even greater pressure on the water bodies of these areas. So far 80% of freshwater biodiversity has been lost. This ongoing trend minimizes the value of such wetland ecosystems drastically. Thus, detailed knowledge of surface water development is needed to enable sustainable water management, safeguard food production, ensure access to clean water and sanitation, and protect respectively restore water-related ecosystems. One of the main indicators of water body change is its area extent, as it can be used to determine long-term and seasonal changes. Due to fast changes in water bodies’ extent, highly frequented long-time observations are required. Deriving this information on a global scale can only be realized via remote sensing. It allows the quick and low cost analysis of large and otherwise inaccessible areas. However, only a few of the remote sensing derived global water products available today have high temporal resolution. So far, most approaches are based on the Terra and Aqua satellites, carrying the Moderate Resolution Imaging Spectroradiometer (MODIS). Using this sensor, Klein et al. (2017) have generated the Global WaterPack: a daily global water map at a spatial resolution of 250m. However, as both satellites have already exceeded their expected lifespan, the applicability of alternative sensors must be investigated, necessitating the selection of satellite missions with at least daily coverage and moderate spatial resolution. Consequently, the objective of this thesis is to investigate the applicability of the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the Suomi National Polarorbiting Partnership (Suomi-NPP) satellite and the Ocean and Land Colour Instrument (OLCI) sensor onboard the Sentinel-3 satellites for this purpose. The key focus is to analyze whether the established Global WaterPack workflow can be applied and which adjustments have to be implemented to create a sensor overlapping long time series. The differences in water classifications are analyzed and subsequently discussed in detail. GWP continuation with VIIRS and OLCI data show an omission error of 9.6% and 12.1%, as well as a Kappa coefficient of 91.3% and 94.5%, respectively.