At the joint invitation of Professor Wu Xiaodan and Researcher Ma Xuanlong from the College of Earth Science and Environmental of Lanzhou University, Hao Dalei, a postdoctoral fellow from the Pacific Northwest National Laboratory, USA, will give an academic presentation to our students and will conduct an academic exchange after the meeting.
Lecturer: Dalei Hao, Postdoctoral Fellow, Pacific Northwest National Laboratory, USA
Title: New advances in modeling radiative transfer in quantitative remote sensing and land surface process models
Time: 20 April 20, 2022 (Wednesday) 9:30-11:00
Tencent Conference: 584 575 346
Lecturer Profile：
Hao Dalei is a postdoctoral fellow at Pacific Northwest National Laboratory, USA. His research interests include quantitative remote sensing, land surface modeling, land-gas interaction, etc. His related research results have been successfully applied to the next-generation Earth System Model-Energy Exascale Earth System Model. In recent years, he has published more than 40 papers in journals such as NREE, Nature Communication, GCB, RSE, ESSD, ISPRS, GMD, JAMES, IEEE TGRS, JGR, etc. He has also served as a reviewer for RSE, GMD, GRL, AFM, JAMES, WRR, JGR, and other international journals.
Report Description:
Radiative transfer is the theoretical cornerstone of quantitative remote sensing and an important basis for modeling surface energy balance and carbon and water cycles in land surface process models. However, most of the current radiative transfer models in quantitative remote sensing and land surface process models contain homogeneous scenario assumptions such as flat surface, uniform coverage, and simple structure, and there is still much room for uncertainty and improvement. This report will use his research as an example, the presentation will briefly introduce the work on global time-by-time solar radiation estimation in quantitative remote sensing, radiative transfer modeling in mountainous areas, angular effects, and normalization of daylight-induced chlorophyll fluorescence (SIF); as well as the progress on the architecture of sub-image heterogeneity representation in land surface process models, the parameterization of solar radiation topographic effects, and the parameterization scheme of snow albedo considering snow grain shape.
