Title: Introduction to NASA Earth Science Remote Sensing Data
Abstract: The volume of publicly available satellite data continues to grow at impressive rates, and the
datasets are providing valuable information to many scientific disciplines and stakeholders. Remote
sensing data records now span multiple decades and are increasingly used in climate science. Statistical
challenges and opportunities abound in working with satellite data, particularly in the areas of spatio-
temporal modeling, missing data, uncertainty quantification, and methods for massive data. This
presentation will introduce some of NASA’s Earth-observing satellite datasets. After an introduction to
NASA’s data processing pipeline and to the self-describing binary data formats used for satellite
products, we will work through exploratory analysis of some example NASA datasets in R. In addition,
we will become familiar with the satellite data archives and the online tools for discovering relevant
datasets before downloading.
Biography
Jon Hobbs is a data scientist at the Jet Propulsion Laboratory, where he works on uncertainty quantification for NASA Earth science datasets. He has developed simulation-based tools for assessing remote sensing retrieval algorithms. Jon completed a co-major PhD in statistics and meteorology at Iowa State University in 2014. His other research interests include development of spatio-temporal statistical methods for geoscience applications and computational methods for Bayesian analysis of hierarchical statistical models for agricultural, environmental and social science applications.
