At the joint invitation of Professor Wu Xiaodan and Researcher Ma Xuanlong from the College of Earth and Environmental Sciences, Lanzhou University, Professor Xu Baodong from Huazhong Agricultural University will give a lecture to our students and have an academic exchange on March 23th, 2022. All teachers and students are welcome to attend!
Speaker: Xu Baodong, Associate Professor (Huazhong Agricultural University)
Report Title: Acquisition of leaf area index products based on satellite remote sensing: Principles, Methods, Practices, and Prospects
Report Time: March 23, 2022 (Wednesday) 14:30-16:10
Tencent Conference: 584 575 346
Lecturer Profile:
Xu Baodong is an associate professor at the College of Resources and Environment, Huazhong Agricultural University, with research interests in quantitative remote sensing of vegetation. He graduated from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences in 2018 for his Ph.D. degree, and received his joint Ph.D. in remote sensing of vegetation at Boston University, the USA from 2016-2018. In recent years, he has published 13 papers on RSE and RS as the first/corresponding author and presided over the projects of the National Natural Science Foundation of China, the sub-projects of the National Key Research and Development Program, and the Natural Science Foundation of Hubei Province. He participated in the formulation of the national standard of "Authenticity Inspection of Leaf Area Index Remote Sensing Products", the writing of the "2014 Annual Report of Global Ecological Remote Sensing Monitoring" and "China Sustainable Development Remote Sensing Monitoring Report (2016)", etc. He was selected as the 2019 Wuhan Yellow Crane Outstanding Young Talents and the 2021 Agricultural Research Outstanding Talents Training Program of the Ministry of Agriculture and Rural Affairs.
Lecture Introduction:
Leaf area index (LAI) is an important biophysical covariate for vegetation growth monitoring and yield estimation. Satellite remote sensing technology provides an effective means to acquire large-area long-time series LAI data sets. The report introduces the theoretical basis and technical means of the "model-inversion-validation" process in LAI remote sensing acquisition and summarizes the research progress of the team in view of the shortcomings of LAI inversion and validation research. Finally, based on LAI products, the report introduces typical cases of vegetation growth dynamics monitoring and driving mechanism analysis in long time series.
