Remote sensing of vegetation structural and physiological parameters for carbon cycle research

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My research program is mainly focused on estimating terrestrial carbon and water cycles through spatially explicit modeling using remote sensing inputs. This proposed research project serves the purpose of developing new algorithms and improving existing algorithms for retrieving land surface parameters useful for regional and global carbon and water cycle modeling. These parameters include leaf area index (LAI), foliage clumping index and physiological reflectance index (PRI). For improving LAI mapping, the main research thrusts are in (i) developing and implementing methods for retrieving vegetation background information using mutiangle remote sensing data and (ii) separating the effects of leaf area and greenness through fusion of remote sensing data with an ecological model. For clumping index, our research focus is on improving its spatial mapping resolution using MODIS BRDF products. PRI, defined as the normalized difference between reflectances at 531 nm and 570 nm, is a new surface parameter to be introduced into our carbon cycle models. Through its relationship with the light use efficiency of plant leaves, PRI will be critically useful for regional validation of plant productivity modeling results, which has not been achieved before. We plan to measure PRI for the whole growing seasons over two forest sites and relate it to carbon flux measurements at these sites. Based on these site-level measurements, an algorithm will be developed to retrieve sunlit and shaded leaf PRI values using data from the space-borne sensor MODIS, and these PRI values will be used to estimate the light use efficiency at regional scale, which is useful for evaluating the spatial distribution of the gross primary productivity estimated using other remote sensing inputs and a process-based model.