Stefan Schlaffer

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Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4). The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the(More)
Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Most available algorithms typically focus on single-image techniques which do not take into account the backscatter signature of a land surface under non-flooded conditions. In this study, harmonic analysis of a multi-temporal time series of >500(More)
Soil moisture is a crucial variable for a large variety of applications with different requirements on the spatial and temporal resolution of the observations. Coarse-scale instruments can provide data operationally with a nearlydaily global coverage at a spatial resolution of several hundreds of square kilometers, whereas SAR instruments provide a spatial(More)
Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface(More)
Wetlands are considered a challenging environment for mapping approaches based on Synthetic Aperture Radar (SAR) data due to their often complex internal structures and the diverse backscattering mechanisms caused by vegetation, soil moisture and flood dynamics contributing to the resulting imagery. In this study, a time series of >100 SAR images acquired(More)
Soil moisture is of high importance in permafrost regions. Within the DUE Permafrost project, adjustments to the 1 km Surface Soil Moisture (SSM) product, derived from ENVISAT ASAR Global Monitoring mode data, have been made to account for some of the conditions encountered at high latitudes. Soil moisture retrieval from SAR requires taking into account the(More)
Predictions of hydrological models are highly uncertain due to both the nature of the modelled system and the meteorological forcings. Soil moisture information derived from satellite data can help to reduce this uncertainty. Indeed, data assimilation techniques offer the possibility to dynamically correct the model evolution in order to improve the model(More)