Rational, Reliable and Repeatable Information Extraction from Remote Sensing Measurements
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Rational, Reliable and Repeatable Information Extraction from Remote Sensing Measurements
Dr. Prabhat Koner
ESSIC
Monday January 22, 2019, 12:00-1:00 PM
ESSIC Conference Room 4102, 5825 University Research Ct, College Park, MD 20740
Abstract:
A tremendous progress in solid-state physics in the past few decades offers highly precise sensors and the understanding of radiative-transfer physics is mature. These offer to grow a ‘big satellite data’ inventory by coordinated-international efforts and tax-payers’ money. These space-based measurements provide a wealth of information for earth science studies. Deriving the information from such measurements depends heavily on the inverse method applied for converting data to information and stochastic inverse methods are dominant in this application. These methods have several drawbacks, such as a high reliance on a priori/in situ information and a frequent higher retrieval error than that of a priori, and a fundamental flaw of treating error as definite information. To overcome these limitations, deterministic approaches are explored and demonstrated for several applications, where rational and repeatable information extraction from remote sensing measurement is achieved. These deterministic methods developed by specialists have matured enough but are limited in application for satellite data due to strong uninhibited dependence on Bayesian probability-based methods employed by major space and environmental agencies. This talk will focus to motivate the community how to overturn the inertia created by the satellite retrieval community, to maximize scientific benefits out of these large investments.
Bio-sketch:
Dr. Koner is an assistant research scientist with the ESSIC at the University of Maryland, College Park. His current research interest is to promote a paradigm shift in the satellite inverse problem for the satellite community to open up a new door in the field of remote sensing. He is PI of NASA ROSES grant. NOAA uses a version of his algorithm in their operational processing of geostationary data. He worked as a research scientist for several universities around the world, namely: EC JRC, Italy; QUT, Australia; Univ. of Toronto, Canada; Univ. of Bonn, Germany and Dalhousie Univ., Canada, on various scientific problems. He earned a PhD degree from the Indian Institute of Technology, Delhi, India.
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