Google Drought Monitoring Through Cloud Computing and Visualization of Remote Sensing and Meteorological Datasets: Examples for California

Wednesday, April 22, 2015
Katherine Hegewisch1, Britta Daudert2, Charles Morton2, Alexander Peterson1, Andrew Joros2, Daniel Mcevoy2, Tyler Erickson3, Justin L Huntington2 and John T Abatzoglou1, (1)University of Idaho, Department of Geography, Moscow, ID, United States, (2)Desert Research Institute, Reno, NV, United States, (3)Google, Mountain View, CA, United States
Drought has adverse effects on society through reduced water availability, agricultural production, and increased wildfire risk. Satellite remote sensing datasets (i.e MODIS and Landsat) can be used to monitor historical and near real-time drought conditions by visualizing vegetation, snow, water, and fire indices computed from at surface reflectance and thermal imagery. Gridded meteorological datasets (i.e. NLDAS, PRISM, DAYMET) can also be used, and paired with remote sensing datasets, to provide information about the causality and intensity of drought conditions. Through a Google Faculty Research Award, we have developed a web application that utilizes Google Earth Engine’s massively parallel cloud computing platform and enables users to process and visualize different drought metrics at multiple time scales and in near real-time. This presentation will illustrate numerous spatial and temporal examples of historical and current California drought conditions using Google App Engine and Google Earth Engine. The ability to access entire remote sensing and meteorological data archives with on demand parallel cloud computing has created numerous opportunities for advanced drought monitoring.