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.
Remote sensing of 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.
Wildfire prediction and its role in the Earth system
Recent wildfires have caused enormous environmental hazards and economic losses. We are working towards using advanced machine learning techniques, Earth system modeling to better understand the patterns, drivers and impacts of the wildfires. The work has been supported by AmFam Data Initiative.
Global monitoring system for wetland CH4 emission
Wetlands are highly dynamic terrestrial-aquatic interfaces widely distributed across tropical, temperate, and high latitude ecosystems. As the largest natural source to the atmosphere, wetlands are responsible for 30% of global methane (CH4) emissions, which accounts for about 25% of cumulative anthropogenic radiative forcing since the industrial revolution. The complexity of wetland CH4 production and consumption processes makes it challenging to upscale and estimate the net emission flux at large scales. We are working to develop a prototype monitoring system of global wetland CH4 emissions and to improve understanding of how the physical environment (i.e., temperature, water, and air pressure, etc.) and vegetation activity affect the spatial and temporal dynamics of CH4 emissions, in collaboration with scientists at Lawrence Berkeley National Laboratory and the University of Illinois at Chicago.