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

我的研究计划的目的是从地球观测中获取新的信息，以解决全球变化问题。本研究的总体目标是在全球尺度上检索叶片水平的光合能力信息，以改善陆地碳和水循环的估算。具体而言，将利用多光谱和高光谱遥感数据检索决定叶片光合能力的叶片最大羧化速率（Vcmax）。从遥感中获取Vcmax的时空信息将对全球碳和水循环研究产生变革性影响，因为目前全球生态和地球系统建模的最新状态是通过植物功能类型（PTF）将Vcmax分配为常数，尽管对于相同的PTF，它可以在空间和季节上变化2-3倍。遥感为Vcmax的时空变化提供了独特的光谱信息，这在全球生态研究中尚未得到充分的探索和利用。通过正在进行的NSERC发现资助（截至2020年3月31日）和其他项目的支持，我们成功探索了利用遥感数据绘制Vcmax的两种方法：(1)利用多光谱光学数据检索叶片叶绿素含量（LCC）并将其转换为Vcmax；(2)利用高光谱卫星传感器测量的太阳诱导叶绿素荧光（SIF）来优化Vcmax。我们利用SIF数据制作了第一个全球Vcmax系列地图，尽管空间分辨率很低（0.5°，赤道约55公里）。在本研究中，我们将通过实现以下目标将这一探索提升到新的高度：(1)开发一种算法，利用光化学反射指数（PRI）图像插值基于SIF的0.5°至1 km分辨率的Vcmax地图，记为SIF+PRI；(2)探讨将新的全球LCC地图转换为Vcmax地图的可行性，从而提供一种比基于sif的Vcmax地图（0.5°分辨率）更高空间分辨率（300 m）的Vcmax独立映射方式；(3)利用地面Vcmax测量数据和高分辨率（20 m）遥感数据对LCC和SIF+PRI获得的Vcmax地图进行评估和验证；(4)利用遥感数据生成的Vcmax地图系列，对比加拿大、美国、欧洲和中国通量塔对一系列PFTs的总初级生产力（GPP）和蒸散发（ET）的地面测量数据，展示陆地碳和水循环模型的改进。本研究将培养1名博士生、2名博士和2名硕士。

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.