Remote Sensing of Vegetation Photosynthetic Capacity and Its Application to Global Carbon and Water Cycle Estimation

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The purpose of my research program is to retrieve new information from Earth observations to address global change issues. The overall goal of this proposed research is to retrieve the leaf-level photosynthetic capacity information at the global scale for improving terrestrial carbon and water cycle estimation. Specifically, the leaf maximum carboxylation rate (Vcmax), which determines leaf photosynthetic capacity, will be retrieved using multi-spectral and hyperspectral remote sensing data. Obtaining spatio-temporal information on Vcmax from remote sensing will have transformative impact on the global carbon and water cycle research because the current state of the art in global ecological and Earth system modelling is to assign Vcmax as constants by plant functional types (PTF), although for the same PTF it can vary 2-3 folds spatially and seasonally. Remote sensing provides unique spectral information for mapping the spatiotemporal variations of Vcmax, which has not yet been much explored and utilized in global ecological research. Through the support of an on-going NSERC discovery grant (ending 31 March 2020) and other projects, we successfully explored two ways of mapping Vcmax using remote sensing data: (1) retrieving leaf chlorophyll content (LCC) using multispectral optical data and converting it to Vcmax; and (2) using solar-induced chlorophyll fluorescence (SIF) measured by hyperspectral satellite sensors to optimize Vcmax. We produced the first ever global Vcmax map series using SIF data, albeit at a coarse spatial resolution (0.5°, about 55 km at the equator). In this proposed research, we will take this exploration to new heights by achieving the following objectives: (1) To develop an algorithm to use images of the Photochemical Reflectance Index (PRI) to interpolate SIF-based Vcmax maps from 0.5° to 1 km resolution, denoted as SIF+PRI; (2) To investigate the feasibility in converting the new global LCC maps into Vcmax maps so as to provide an independent way of mapping Vcmax at a higher spatial resolution (300 m) than the SIF-based Vcmax maps (0.5° resolution); (3) To evaluate and validate these Vcmax maps derived from LCC and SIF+PRI using ground-based Vcmax measurements and high resolution (20 m) remote sensing data; and (4) To demonstrate the improvements in terrestrial carbon and water cycle modeling using the Vcmax map series generated using remote sensing data against ground measurements of gross primary productivity (GPP) and evapotranspiration (ET) at flux towers in Canada, USA, Europe and China, for a range of PFTs. This proposed research will provide an opportunity to train 1 PDF, two Ph.D. and two M.Sc. students.