TWC Seminar, Dr. Qiaozhen Mu

University of Montana

 

MODIS global terrestrial evapotranspiration (ET) algorithm uses MODIS land cover, albedo, FPAR/LAI and global meteorological reanalysis data as inputs to estimate ET.  The regular 1-km2 8-day, monthly and annual global MODIS ET products include evapotranspiration (ET)/latent heat flux (LE)/potential ET (PET)/potential LE (PLE) datasets.  MODIS ET products have been validated over flux tower sites and global watersheds, demonstrating the robustness of MODIS ET algorithm.  A remotely sensed global terrestrial drought severity index (DSI) was developed by combining MODIS ET and NDVI products. The MODIS DSI and ET, together with the global terrestrial primary production (GPP/NPP) data product, are valuable and effective for drought monitoring and assessment of how water supply and ecosystems respond to droughts, that is how water balance, crop yields, forest productivity and carbon sequestration are affected by droughts.  The remotely-sensed global terrestrial DSI and ET products will help to improve the accuracy of current widely used drought monitoring methods in the near-real time drought monitoring systems, and will help the decision makers to take prompt actions to adapt and mitigate the adverse effects of droughts.
 
Brief Biography
Dr. Mu has a bachelor degree in Physics, a master degree in Geography and a PhD in Climatology.  Dr. Mu worked on earth system models at University of Texas, Austin for two years before she joined University of Montana to work with Dr. Steve Running in 2003.  Her current research expertise and interests involve carbon, water and energy interactions between land surface and atmosphere using remote sensing models and ecosystem process-based models regionally and globally.  As the core science developer for NASA’s MODIS 16 evapotranspiration algorithm, she together with her colleagues generated the first regular 1-km2 global evapotranspiration product for the global vegetated land areas at 8-day, monthly and annual intervals.  She and her colleagues recently proposed a remotely sensed global terrestrial drought severity index to monitor drought.