Min Chen

Prof. Min Chen

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Assistant Professor

Russell Labs
1630 Linden Drive
Madison, WI 53706
Phone: 
E-mail: mchen392@wisc.edu

Lab Website

Expertise

Terrestrial ecosystem/land surface modeling, remote sensing theory and applications, human-Earth system interactions

Education/Experience

Education

Degree     
Institution Major Field Granted
PhD Purdue University Earth & Atmospheric Sciences 2013
MS Beijing Normal University Remote Sensing and GIS 2008
BS Beijing Normal University Computer Science 2005

Professional Experience

Institution Title Years
Department of Global Ecology, Carnegie Institution for Science at Stanford University Barbara McClintock Fellow 2015-2016
Department of Organismic and Evolutionary Biology, Harvard University Postdoctoral Fellow 2013-2015

Research

Current Projects

Arctic-Boreal ecosystem changes and the socioeconomic impacts

We are leading a project that investigates the critically important but poorly understood ecosystem changes in the Arctic-Boreal ecosystems, as well as the associated socioeconomic impacts. We are using a variety of observations in an integrated framework consisting of an advanced land surface model (the Community Land Model, CLM), a sophisticated integrated assessment model (the Global Change Assessment Model, GCAM), and the Data Assimilation Research Testbed (DART) to quantify how the observation data obtained by the Arctic Boreal Vulnerability Experiment (ABoVE) can constrain the terrestrial component of Human-Earth system models, characterize uncertainties in their projections, and assess the socioeconomic impacts of such improved projections at regional and global scales.

photo credit: NASA

Solar-induced Chlorophyll Fluorescence

One of the overarching goals of vegetation remote sensing is to provide spatially resolved information to support simulation of the photosynthesis rate of the terrestrial biosphere. The recent successes of solar induced fluorescence (SIF) retrievals as a proxy for GPP across different vegetation types and with different environmental or physiological limitations, showing that there is potentially more information in remote sensing data than previously thought. Similar to other optical signals, SIF is reabsorbed and scattered within the canopy, and these radiative transfer processes contribute to the remotely-sensed SIF signals. Some key questions remain to be answered, such as: What is the role of canopy structure in the correlation between remotely-sensed SIF and vegetation photosynthesis? And, how can we minimize the sun-sensor geometry effects on SIF? Can we retrieve leaf-scale SIF and related parameters (e.g., light use efficiency for SIF, namely SIF yield)? Further, if we can do this, will these advances have follow-on impacts on our ability to use remote sensing for other purposes?

We are leading a project funded by NASA's Remote Sensing Theory Program to pursue answers to the above questions. We are developing a novel leaf-canopy radiative transfer model to improve our understanding of radiative transfer as relate to SIF, and in turn obtain new insights of remote sensing of vegetation properties.

photo credit: NASA

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Courses Taught

Publications

Selected Publications

A list of his publications on Google Scholar can be found here.