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Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse eco-social impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. Mu et al. (2013) developed a method to generate a near-real-time remotely sensed Drought Severity Index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly and annual frequencies. The DSI integrates and exploits information from current operational satellite based terrestrial evapotranspiration (ET) and Vegetation greenness Index (VI) products, which are sensitive to vegetation water stress. Specifically, our approach determines the annual DSI departure from its normal (20002011) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). Based on the global MOD16 ET algorithm, Mu et al. (in preparation) have further improved the MODIS ET algorithm for Nile River Basin Countries. Not only are the results improved dramatically but also the data cover every 1-km pixel of the land surfaces including inland waters (lakes, rivers, etc.), deserts, urban areas, unclassified land surfaces, etc. Using the improved MODIS ET algorithm, we can generate remotely sensed terrestrial ET and DSI products with higher quality. These products will enhance our capabilities for near-real-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.