Prior to joining LSGI, Dr. Yin was a research scientist jointly at NASA Goddard Space Flight Center (GSFC) and the Earth System Science Interdisciplinary Center of the University of Maryland. Before that, he was a SMART Scholar Postdoctoral Fellow at the Singapore-MIT Alliance for Research and Technology (SMART). He obtained his B.Sc. in Physics and M.Eng. in Computer Engineering from National University of Singapore (NUS), and his Ph.D. in Remote Sensing and Geoscience from the Centre d'Etudes Spatiales de la BIOsphère (CESBIO), CNES-CNRS-IRD-UPS, Toulouse, France.
Dr. Yin has been leading the developments of the 1) LiDAR, 2) photogrammetry, and 3) atmosphere modules in the 3-D discrete anisotropic radiative transfer (DART) model since 2011. At GSFC, he was a task leader for the “Solar-Induced Fluorescence Airborne Research Experiment”. He developed the PVlad model for 3-D realistic reconstruction of forests using airborne LiDAR data. He participated in the pre-launch modeling study of NASA’s future LiDAR-Stereo satellite based on the Surface Topography and Vegetation (STV) scheme under the Decadal Survey Incubation program by integrating PVlad with DART. He also led the “Computerized Management of Urban Trees” project (2017-2021) supported by the Singapore Ministry of National Development: a UAV-based hyperspectral remote sensing platform was built to monitor the health status of urban individual trees.
Dr. Yin would invite self-motivated postgraduate students and postdocs to join the team of 3-D sensing, modeling, and data intelligence. Please contact Dr. Yin for more information.
Quantitative Remote Sensing,
3-D radiative transfer modeling (forward and inverse),
3-D urban/forest reconstruction using LiDAR and stereo photogrammetry,
Urban individual tree monitoring and management,
Urban climate and ecosystem services,
Physical modeling of passive/active sensor mounted on satellite/aircraft/mobile platform,
Solar-induced chlorophyll fluorescence,
Science-driven pre-launch study for Earth-Observation satellites,
Physically based ray tracing,
Big-data driven global photosynthesis modeling.
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