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My research interest is in land-atmosphere interactions particularly focusing at the ecophysiological factors and atmospheric chemistry.

About me

I am a second year graduate student in Dr. Ying Sun's lab. Prior, I did my undergraduate study at Beijing Normal University with a double major in Resources and Environmental Science and International Economic and Trade. During my undergraduate study, I was advised by Dr. Jing Yang to evaluate the extreme high temperature Days (EHD) among CMIP5 climate models. I also did a visiting research with Dr. Yuzhong Zhang at the Westlake University to estimate ammonia (NH3) emissions by integrating satellite observations and model simulation.

I am a fan of traveling, nature exploring, sports, and cooking. My favorite sports are hiking, running and cycling, since I like the extraordinary views during the exercise, and that is one of reasons I choose to live besides a farm in Ithaca.


(2021 AGU, New Orleans)

Research Projects:


Estimate global NH3 emissions

Background: Emissions of NH3 to the atmosphere impact human health, climate, and ecosystems via their critical contributions to secondary aerosol formation. However, the estimation of NH3 emissions is associated with large uncertainties because of inadequate knowledge about agricultural sources.

We use satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) and simulations from the GEOS-Chem model to constrain global NH3 emissions over the period from 2008 to 2018. We found that in contrast to the approximate factor of 2 discrepancies between top-down and bottom-up emissions found in previous studies, our method results in a global land NH3 emission of 78 (70–92) Tg a−1, which is ∼30 % higher than the bottom-up estimates.


Understand the driving factors for SIF-GPP dynamics at canopy scale

Background: Recent breakthrough of remote sensing SIF techniques offers a promising possibility to infer GPP, which is difficult to directly measure at scales beyond a single leaf. However, incorporating satellite SIF to estimate GPP still heavily depends on empirical approaches.

We use a mechanistic model to infer GPP fluxes from SIF, utilizing high-resolution satellite SIF retrievals, complemented with hyperspectral reflectance spectra from airborne campaigns. Our approach demonstrates the potential towards a global GPP estimate from satellite SIF in a mechanistic manner.

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