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检索新的信息，以解决全球变化问题。这项拟议研究的总体目标是在全球尺度上提取叶片水平的光合作用能力信息，以改进陆地碳和水循环估计。具体地说，决定叶片光合作用能力的叶片最大羧化速率(Vcmax)将使用多光谱和高光谱遥感数据进行反演。从遥感获取Vcmax的时空信息将对全球碳和水循环研究产生革命性的影响，因为全球生态和地球系统模拟的现状是根据植物功能类型(PTF)将Vcmax指定为常量，尽管对于相同的PTF，它可以在空间和季节上变化2-3倍。遥感为绘制Vcmax的时空变化提供了独特的光谱信息，但在全球生态研究中还没有得到太多的探索和利用。通过正在进行的NSERC发现拨款(截至2020年3月31日)和其他项目的支持，我们成功地探索了两种利用遥感数据绘制Vcmax的方法：(1)使用多光谱光学数据提取叶片叶绿素含量(LCC)并将其转换为Vcmax；(2)使用高光谱卫星传感器测量的太阳诱导的叶绿素荧光(SIF)来优化Vcmax。我们使用SIF数据制作了有史以来第一个全球Vcmax地图系列，尽管空间分辨率较低(0.5°，赤道约55公里)。在这项拟议的研究中，我们将通过实现以下目标将这一探索推向新的高度：(1)开发一种算法，利用光化学反射指数(PRI)图像来内插基于SIF的0.5°到1公里分辨率的Vcmax地图，记为SIF PRI；(2)研究将新的全球LCC地图转换为Vcmax地图的可行性，以便提供一种独立的方法来绘制空间分辨率(300 M)高于基于SIF的Vcmax地图(0.5°分辨率)的Vcmax地图；(3)利用地面Vcmax测量和高分辨率(20米)遥感数据，评估和验证从LCC和SIF PRI得到的这些Vcmax图；以及(4)展示使用遥感数据生成的Vcmax图系列与地面测量的加拿大、美国、欧洲和中国的通量塔的总初级生产力(GPP)和蒸散量(ET)相比，对一系列PFT的陆地碳和水循环模拟的改进。这项拟议的研究将提供培训1名PDF、2名博士和2名硕士的机会。学生们。

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.