Global Mapping of Vegetation Physiological Parameters

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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.**