Global Mapping of Vegetation Physiological Parameters

植被生理参数的全球制图

• 批准号: RGPIN-2015-04066
• 负责人: Chen, Jing
• 金额: $ 2.4万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=647347
• 关键词: Global; Mapping; Vegetation; Physiological; Parameters

In my previous project entitled "Remote Sensing of Vegetation Structural and Physiological Parameters" funded by a NSERC discovery grant, algorithms were refined for retrieving vegetation structural parameters (leaf area index and clumping index) from multi-angle remote sensing data and new algorithms were developed for deriving physiological parameters (chlorophyll content, light use efficiency) of plant leaves using hyperspectral remote sensing data. Results of this project indicate that it is feasible to derive these physiological parameters by remote sensing means. This proposed research will expand the work to the retrieval of more physiological parameters including the maximum carboxylation rate (Vcmax) and chlorophyll fluorescence (Fs). The spatial information of these physiological parameters is critically useful for ecological studies. For example, chlorophyll is responsible for light harvesting in plant leaves, while Vcmax defines the capacity of plant leaves in converting harvested light energy into carbohydrate. The light use efficiency remotely sensed at one time in one spatial unit (pixel) can be used to validate photosynthesis models. Currently, advanced ecological models developed for regional and global applications use only constant Vcmax values (normalized to 25 degree C) for different plant functional types (PTF) because no spatial information for this parameter is available. For the same PTF, this parameter can vary by a factor of 2-3, causing large distortions in the estimated spatial distribution of vegetation productivity and its related carbon cycle components. If both chlorophyll content and the maximum carboxylation rate can be mapped using remote sensing techniques, regional and global carbon and water cycle estimations can be greatly improved. The objectives of the proposed study are: (1) To produce a global map of chlorophyll content using data from the pseudo-hyperspectral sensors MERIS and Sentinel-2. For this purpose, more data will be collected from crop, grass and forest ecosystems to validate and further develop our chlorophyll retrieval algorithm; (2) To produce global maps of leaf Vcmax using MODIS data. For this purpose, MODIS data in band 11 and band 12 will be processed to obtain canopy-level Photochemical Reflectance Index from which to derive Vcmax; (3) To investigate chlorophyll fluorescence (Fs) as a proxy for photosynthetic rates; and (4) To improve global mapping of vegetation productivity by exploring ways to combine Fs, Vcmax and chlorophyll content for improved photosynthesis modelling, and to validate the results against ground measurements at flux towers in Canada, USA, Europe and China, for a range of different plant functional types.**

在我以前的项目，题为“遥感植被结构和生理参数”由NSERC发现基金资助，算法进行了改进，从多角度遥感数据检索植被结构参数（叶面积指数和丛生指数）和新的算法开发用于推导植物叶片的生理参数（叶绿素含量，光利用效率）使用高光谱遥感数据。结果表明，利用遥感手段获取这些生理参数是可行的。本研究将拓展到最大羧化速率（Vcmax）和叶绿素荧光（Fs）等生理参数的反演。这些生理参数的空间信息对于生态学研究是非常有用的。例如，叶绿素负责植物叶片中的光收集，而Vcmax定义了植物叶片将收集的光能转化为碳水化合物的能力。在一个空间单位（像元）内的光利用效率可以用来验证光合作用模型。目前，为区域和全球应用开发的高级生态模型仅使用不同植物功能类型（PTF）的恒定Vcmax值（标准化为25摄氏度），因为该参数没有空间信息。对于相同的PTF，这一参数可以变化2-3倍，造成植被生产力及其相关碳循环组成部分的估计空间分布的巨大扭曲。如果能够利用遥感技术绘制叶绿素含量和最大羧化率的地图，就可以大大改进区域和全球碳和水循环的估计。拟议研究的目标是：（1）利用伪高光谱传感器MERIS和Sentinel-2的数据制作全球叶绿素含量图。为此，将收集更多的数据，从作物，草地和森林生态系统，以验证和进一步发展我们的叶绿素反演算法;（2）使用MODIS数据制作全球地图的叶片Vcmax。为此，将对MODIS波段11和波段12的数据进行处理，以获得冠层水平的光化学反射指数，并由此获得Vcmax：（3）研究叶绿素荧光（Fs）作为光合速率的替代指标;（4）探索如何将Fs、Vcmax和叶绿素含量结合起来，改进光合作用模型，并根据加拿大、美国、欧洲和中国通量塔的地面测量结果，对一系列不同的电厂功能类型进行验证。**