The near-infrared reflectance of vegetation
Keywords: 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, largely due to uncertainties in the amount of light, water, and other resources plants have available to fix carbon. At these larger scales, satellite remote sensing has emerged as a leading technique for inferring plant productivity. It is generally accepted that satellites provide robust estimates of the total amount of light absorbed by plants, which serves as a useful starting point for estimating photosynthesis. However, there does not exist a broadly accepted approach for quantifying how plant photosynthetic capacity varies in both time and space. In this dissertation, I present an approach for estimating photosynthesis using measurements of the near-infrared reflectance of vegetation (NIRv). Over the course of four chapters, I show that NIRv contains information about how plants build and display their leaves and demonstrate that measurements of canopy architecture convey information about canopy photosynthetic capacity. In Chapter 1, I explore the history of near-infrared (NIR) remote sensing and discuss how modern day remote sensing relies on patterns of NIR reflectance to study plant productivity. I also discuss how NIR measurements are uniformly contaminated by radiation reflected by the soil, not vegetation. Such contamination induces variations in NIR reflectance that are unrelated to plant physiology and productivity, which complicates our ability to relate observations of NIR to photosynthesis. Chapter 2 introduces NIRv, a new reflectance-based remote sensing approach that minimizes the influence of soil contamination. I show that NIRv is a robust predictor of both modeled and in situ estimates of photosynthesis and that NIRv is strongly correlated with solar-induced chlorophyll fluorescence, a measurement of canopy light capture that is also unaffected by soil contamination. Chapter 3 explores the physical basis of the NIRv signal, demonstrating that NIRv is jointly dependent on total leaf area, leaf reflective properties, and the fraction of NIR photons that escape from the canopy. This physical model of NIRv indicates that variations in the escape probability of NIR photons are essential to understanding the empirical relationship between NIRv and photosynthesis. Chapter 4 combines site-level measurements of both photosynthesis and NIRv to produce a simple, NIRv-based statistical model capable of estimating monthly and annual photosynthetic fluxes with accuracy that rivals existing approaches for approximating global photosynthesis. Together, the four chapters of my dissertation form the foundation for using NIRv as a new tool for studying photosynthesis at the canopy scale and beyond. One of the primary advantages of NIRv is 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. My dissertation emphasizes the central role that canopy architecture plays in plant productivity, which stands in contrast to 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, such as NIRv, capture the dynamics of biochemistry. This presents the opportunity to use satellite measurements of NIRv to drive ecosystem models and, ultimately, improve our understanding of the physiological and evolutionary processes that govern whole-plant resource use.