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

植被光合能力遥感及其在全球碳水循环估算中的应用

• 批准号: RGPIN-2020-05163
• 负责人: Chen, Jing
• 金额: $ 4.44万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754852
• 关键词: Remote; Sensing; Vegetation; Photosynthetic; Capacity

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.

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。本研究的总体目标是在全球范围内恢复叶片光合能力信息，以提高陆地碳和水循环的估计。具体而言，叶片最大羧化率（Vcmax），决定叶片光合能力，将使用多光谱和高光谱遥感数据检索。从遥感中获得Vcmax的时空信息将对全球碳和水循环研究产生变革性影响，因为全球生态和地球系统建模的当前最新技术是将Vcmax指定为植物功能类型（PTF）的常数，尽管对于同一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，这在全球生态研究中尚未得到太多的探索和利用。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，我们成功探索了使用遥感数据绘制Vcmax的两种方法：（1）使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;和（2）使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）。在本研究中，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Vcmax地图，从0.5°到1 km分辨率，表示为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式来以更高的空间分辨率（300 m）绘制Vcmax。比基于SIF的Vcmax图（3）利用地面Vcmax测量和高分辨率（20米）遥感数据，评价和验证从LCC和SIF+PRI得出的Vcmax图;（4）利用遥感数据生成的Vcmax图系列，对照总初级生产力的地面测量值，展示陆地碳和水循环建模的改进（GPP）和蒸散量（ET）在通量塔在加拿大，美国，欧洲和中国，为一系列的PFT。这项拟议的研究将提供一个机会，培养1名PDF，两名博士。和两名理学硕士