Research interests: terrestrial vegetation dynamics and feedback, remote sensing, machine learning and causal inference
Ph.D., 2017-2023 (expected), Soil and Crop Science, Cornell University
B.Eng., 2013-2017, Hydrology and Hydraulic Engineering, Tsinghua University
I am interested in investigating terrestrial vegetation dynamics and its relationships with environmental variables, at diurnal, seasonal, and interannual scales. Various data streams are used in my research, including eddy-covariance flux data, remote-sensing datasets, reanalysis products, and Earth System Models (ESMs)’ output. Besides, I have developed various remote-sensing products based on original satellite retrievals. For example, I have developed long-term high-resolution (0.05 degree) global SIF products using machine learning algorithms, which greatly resolved the limitations of original satellite retrievals (e.g., coarse spatial resolution, incomplete global coverage). I have also established a framework to generate high-resolution (70m) diurnally-resolved LST and ET products from sporadic ECOSTRESS samplings. These products can substantially facilitate vegetation/water monitoring in ecological and agricultural applications.
Outside my research, I like reading books, jogging, hiking and enjoying the beautiful waterfalls around Ithaca.
Global map of SIF_005 in July 2010. US Corn Belt (inset) showed the largest SIF emission during the growing season. Details can be found in Wen et al. (2020)
The constructed ECOSTRESS LST and ET mapping at 70m hourly resolution, at a test domain in Sevilleta Wildlife Refuge during 26 September and 5 October 2020. Details can be found in Wen et al. (2022)