WIT Webinar: What is the Optimal Mixture of Space-borne Sensors for Remote Sensing of Terrestrial Freshwater?: A Comparative Analysis of Passive Optical, Passive Microwave, Active Microwave, and LiDAR Retrievals

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The Centre for Water Informatics and Technology presents a virtual seminar titled: "What is the Optimal Mixture of Space-borne Sensors for Remote Sensing of Terrestrial Freshwater?: A Comparative Analysis of Passive Optical, Passive Microwave, Active Microwave, and LiDAR Retrievals" as part of its "Models, Learning and Sensing in Hydrology" seminar series

Wed, Feb 23 2022 to Wed, Feb 23 2022

online

Date: Wednesday, February 23, 2022

Time: 6:00 – 7:00 pm PKT

Speaker: Dr. Bart Forman,

Associate Professor, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD

Moderator: Dr. Jawairia Ashfaq Ahmad, WIT

Details and registration: https://wit.lums.edu.pk/MLSH2022

 

Abstract: An observing system simulation experiment (OSSE) is used to evaluate different observing strategies of terrestrial freshwater and to help create a mission planning tool with a particular focus on the coupled snow-soil moisture-vegetation system at the land surface. The NASA Land Information System (LIS) is used to model the spatiotemporal dynamics of the hydrologic cycle. LIS is then linked to the Trade-space Analysis Tool for Constellations (TAT-C) in order to provide a realistic view of land surface conditions as seen by a given space-borne sensor that is then used to generate synthetic, space-borne retrievals of snow, soil moisture, and vegetation. The merger of LIS with TAT-C enables the simulation of different mixtures of space-borne sensors designed to explore the trade-off in scientific utility related to passive microwave radiometry, microwave RADAR, and optical LiDAR. An ensemble-based data assimilation framework is employed to systematically merge (in a Bayesian sense) the land surface model with the space-borne retrievals of snow, soil moisture, and/or vegetation. The results from the OSSE enable systematic comparisons across a wide range of different observing system strategies and can help program managers decide what is the optimal mixture of sensor types, orbital configurations, senor resolutions, and instrument errors in order to achieve a predefined scientific benchmark. The end result of this project will be a framework to help decide how to harness the information content of Earth science mission data in order to best characterize the spatiotemporal dynamics of freshwater in the natural environment.

About the Speaker:

Dr. Bart Forman is currently an associate professor of civil engineering at the University of Maryland. He completed his undergraduate studies in Civil Engineering at the University of Virginia and then pursued his master’s and doctorate degrees in Civil Engineering at UC Berkeley and UCLA, respectively. Upon completion of his Ph.D. in 2010, Dr. Forman was awarded a NASA Postdoctoral Fellowship at the Goddard Space Flight Center in Greenbelt, Maryland where his research focused on the assimilation of terrestrial water storage estimates derived from the Gravity and Recovery and Climate Experiment (GRACE) mission into an advanced land surface model to improve snow water equivalent estimates in Alaska and Canada. In January 2012, Dr. Forman joined the faculty of the Department of Civil and Environmental Engineering at the University of Maryland where his research group focused on the utilization of space-based instrumentation to study freshwater on the Earth’s surface across regional and continental scales. Dr. Forman was awarded a NASA New Investigator Award in 2014, named a member of the NASA GRACE / GRACE-FO Science Team in 2015, received the Edward Kent Jr. Teaching Award for Excellence in Engineering Instruction in 2016, named a member of the NASA High Mountain Asia Science Team in 2016, was honored as an Outstanding Mentor for Engineers Without Borders in 2018, was awarded a Fulbright Scholarship to conduct research in India during 2019-202, named a member of the NASA GRACE-FO Science Team in 2020, and named a member of the NASA High Mountain Asia Science Team2 in 2020. Dr. Forman’s current research interests include the use of artificial intelligence and machine learning algorithms to diagnose passive microwave observations over snow-covered land in conjunction with space-borne measurements of the Earth’s gravitational field to better diagnose terrestrial water storage during periods of flood or drought. He has published more than 45 articles in peer-reviewed journals and has contributed to more than 120 presentations at national and international conferences.

For details or queries, please contact Soban Hameed Saigol at soban.hameed@lums.edu.pk or 0332 4495057

Add to Calendar 2022-02-23 18:00:00 2022-02-23 19:00:00 WIT Webinar: What is the Optimal Mixture of Space-borne Sensors for Remote Sensing of Terrestrial Freshwater?: A Comparative Analysis of Passive Optical, Passive Microwave, Active Microwave, and LiDAR Retrievals Date: Wednesday, February 23, 2022 Time: 6:00 – 7:00 pm PKT Speaker: Dr. Bart Forman, Associate Professor, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD Moderator: Dr. Jawairia Ashfaq Ahmad, WIT Details and registration: https://wit.lums.edu.pk/MLSH2022   Abstract: An observing system simulation experiment (OSSE) is used to evaluate different observing strategies of terrestrial freshwater and to help create a mission planning tool with a particular focus on the coupled snow-soil moisture-vegetation system at the land surface. The NASA Land Information System (LIS) is used to model the spatiotemporal dynamics of the hydrologic cycle. LIS is then linked to the Trade-space Analysis Tool for Constellations (TAT-C) in order to provide a realistic view of land surface conditions as seen by a given space-borne sensor that is then used to generate synthetic, space-borne retrievals of snow, soil moisture, and vegetation. The merger of LIS with TAT-C enables the simulation of different mixtures of space-borne sensors designed to explore the trade-off in scientific utility related to passive microwave radiometry, microwave RADAR, and optical LiDAR. An ensemble-based data assimilation framework is employed to systematically merge (in a Bayesian sense) the land surface model with the space-borne retrievals of snow, soil moisture, and/or vegetation. The results from the OSSE enable systematic comparisons across a wide range of different observing system strategies and can help program managers decide what is the optimal mixture of sensor types, orbital configurations, senor resolutions, and instrument errors in order to achieve a predefined scientific benchmark. The end result of this project will be a framework to help decide how to harness the information content of Earth science mission data in order to best characterize the spatiotemporal dynamics of freshwater in the natural environment. About the Speaker: Dr. Bart Forman is currently an associate professor of civil engineering at the University of Maryland. He completed his undergraduate studies in Civil Engineering at the University of Virginia and then pursued his master’s and doctorate degrees in Civil Engineering at UC Berkeley and UCLA, respectively. Upon completion of his Ph.D. in 2010, Dr. Forman was awarded a NASA Postdoctoral Fellowship at the Goddard Space Flight Center in Greenbelt, Maryland where his research focused on the assimilation of terrestrial water storage estimates derived from the Gravity and Recovery and Climate Experiment (GRACE) mission into an advanced land surface model to improve snow water equivalent estimates in Alaska and Canada. In January 2012, Dr. Forman joined the faculty of the Department of Civil and Environmental Engineering at the University of Maryland where his research group focused on the utilization of space-based instrumentation to study freshwater on the Earth’s surface across regional and continental scales. Dr. Forman was awarded a NASA New Investigator Award in 2014, named a member of the NASA GRACE / GRACE-FO Science Team in 2015, received the Edward Kent Jr. Teaching Award for Excellence in Engineering Instruction in 2016, named a member of the NASA High Mountain Asia Science Team in 2016, was honored as an Outstanding Mentor for Engineers Without Borders in 2018, was awarded a Fulbright Scholarship to conduct research in India during 2019-202, named a member of the NASA GRACE-FO Science Team in 2020, and named a member of the NASA High Mountain Asia Science Team2 in 2020. Dr. Forman’s current research interests include the use of artificial intelligence and machine learning algorithms to diagnose passive microwave observations over snow-covered land in conjunction with space-borne measurements of the Earth’s gravitational field to better diagnose terrestrial water storage during periods of flood or drought. He has published more than 45 articles in peer-reviewed journals and has contributed to more than 120 presentations at national and international conferences. For details or queries, please contact Soban Hameed Saigol at soban.hameed@lums.edu.pk or 0332 4495057 online LUMS Drupal 8 adil.sarwar@lums.edu.pk Asia/Karachi public

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