Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007-2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people's rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood

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In recent years, the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) has gained a lot of experience in water surface extraction from synthetic aperture radar (SAR) data for various application domains. In this context, four approaches have been developed, which jointly form the so-called DFD Water Suite: The Water Mask Processor (WaMaPro) is based on a simple and highperformance algorithm that processes multi-sensor SAR data in order to provide decision-makers with information about the location of water surfaces. The Rapid Mapping of Flooding tool (RaMaFlood) has been developed for flood extent mapping using an interactive object-based classification algorithm. The TerraSAR-X Flood Service (TFS) is used for rapid mapping activities and provides satellite-derived information about the extent of floods in order to support emergency management authorities and decisionmakers. It is based on a fully automated processing chain. The last approach is the TanDEM-X Water Indication Mask processor (TDX WAM). It is part of the processing chain for the generation of the seamless, accurate, and high-resolution global digital elevation model (DEM) produced based on data of the TanDEM-X mission. Its purpose is to support the subsequent DEM editing process by the generation of a global reference water mask. In this study, the design of the four approaches and their methodological backgrounds are explained in detail, while simultaneously elaborating on the preferred application domains for the different algorithms. The advantages and disadvantages of the four approaches are identified by qualitatively as well as quantitatively evaluating the water masks derived from data of the TanDEM-X mission for five test sites located in Vietnam, China, Germany, Mali, and the Netherlands.

We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT) for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment. Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS), as introduced by the Open Geospatial Consortium (OGC), are utilized. TWOPAC's functionality to process geospatial raster or vector data via web resources (server, network) enables 2531TWOPAC's usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user's perspective.

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%.