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Hello

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

About me

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.

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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)

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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)