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Title:   Author  item
                                            HOUBORG, RASMUS - 1265-60-00item
                                            BOEGH, EVA - UNIV. OF ROSKILDEitem
                                            Anderson, Martha
  Submitted to: BARC Poster Day
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 3/26/2007
                                    Publication  Date: 4/25/2007
                                    Citation: Houborg, R., Boegh, E., Anderson, M.C. 2007. A fast scheme for mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data [abstract]. Abs. 15. BARC Poster Day.Interpretive Summary:
                                Technical Abstract: Reflectance data in the green, red and near-infrared wavelengths were acquired by the SPOT satellite over an agricultural area in Denmark for the purpose of estimating leaf chlorophyll content green leaf area index (LAI).  SPOT reflectance observations were atmospherically corrected and used as input for the inversion of a canopy reflectance model to retrieve soil and crop-specific parameters. These parameters were then used in forward modeling mode to build multiple species- and site-dependent formulations relating LAI to vegetation indices or single spectral band reflectances. Subsequently, the family of model generated relationships, each a function of soil background and canopy characteristics, was employed for a fast pixel-wise mapping of LAI. The biophysical parameter retrieval scheme is completely automated and image-based and solves for the soil background reflectance signal, leaf mesophyll structure, specific dry matter content, Markov clumping characteristics, LAI without utilizing calibration measurements. Despite the high vulnerability of near-infrared reflectances to variations in background properties, an efficient correction for background influences and a strong sensitivity of to LAI, caused LAI relationships to be very useful and preferable over LAI-NDVI relationships for LAI prediction when LAI >2. Reflectances in the green waveband were chosen for producing maps. The application of LAI – NDVI, LAI and relationships provided reliable quantitative estimates of  LAI for agricultural crops characterized by contrasting architectures and leaf biochemical constituents with overall root mean square deviations between estimates and in-situ measurements of 0.74 for LAI and 5.0 'g cm-2. 
Further testing of the model is being planned to evaluate the usefulness and limitations of the approach for environments and species compositions typical of the continental United States. The spatial estimates of leaf chlorophyll may aid a spatial parameterization of photosynthetic capacity (i.e. the Rubisco capacity), which is a key input to ecosystem models implemented with the widely used leaf photosynthesis model of Farquhar et al. (1980).


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