Stanford University

ESS Ph.D. Dissertation Defense: The Near-Infrared Reflectance of Vegetation by Grayson Badgley

Thursday, Mar 14, 2019 12:00 PM
Y2E2 300
Faculty/Staff, Students, Alumni/Friends
Department of Earth System Science

Photosynthesis drives global biogeochemical and hydrological cycles, making efforts to quantify photosynthesis central to improving our understanding of biodiversity, agriculture, and ecosystem function at the global scale. However, beyond the scale of a single leaf, our ability to measure photosynthesis remains highly uncertain. In this dissertation, I present a new approach for estimating photosynthesis using satellite measurements of the near-infrared reflectance of vegetation.

I will introduce the near-infrared reflectance of vegetation (NIRV), a new reflectance-based remote sensing approach that is strongly correlated with solar-induced chlorophyll fluorescence (SIF; a measurement physically related to whole-canopy light capture). I show that NIRVis also a robust predictor of both modeled and in situestimates of photosynthesis and discuss the ecological and radiative transfer theory underpinning these observed correlations. One of the primary advantages of NIRVis that it can be calculated using existing satellite sensors, opening the possibility of producing satellite-derived photosynthesis estimates at the global scale going back decades. I will present results from a simple, NIRV-based statistical model that produces monthly and annual photosynthesis estimates that are comparable in accuracy to widely used machine-learning approaches.

My dissertation emphasizes the central role that canopy architecture plays in plant productivity, which stands in contrast to existing efforts to explain patterns of global photosynthesis as a product of canopy biochemistry. While biochemistry is important, my work indicates that measurements of canopy architecture can be used to capture dynamics in biochemistry. This opens the door to directly using satellite measurements of NIRVto drive ecosystem models and, ultimately, improve our understanding of the physiological and evolutionary processes that govern whole-plant resource use.

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