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28 January 2002 Assessment of inputs to land surface processes models derived from hyperspectral multiangular data
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Global Models of the Earth - Atmosphere System describe the role of the terrestrial biosphere using increasingly complex Land Surface Models (LSM). These models mimic the exchange of energy, water and carbon between the land and the atmosphere, with emphasis on the role of terrestrial vegetation. Literature shows a clear trend towards fully interactive LSM-s, i.e. accounting for the dynamic response of vegetation to weather and climate. The latter may not be limited to biomass accumulation and address slower changes in vegetation type and composition. Improving the performance of such models require addressing two broad questions: Can we measure vegetation properties with the accuracy required by model sensitivity? How do we measure vegetation properties over the grid size of Global Models and are we able to incorporate the inherent spatial heterogeneity of terrestrial vegetation? The role of terrestrial vegetation in the land - atmosphere exchanges of energy, water and carbon is determined by properties, such as albedo, fAPAR, LAI and chlorophyll, related to spectro - directional radiance in the range 0.4 im - 14 im.. These variables and their spatial patterns can, therefore, be determined with accurate observations of spectro - directional radiance at selected view - angles and wavelengths. The paper summarizes results of several field experiments and airborne campaigns in Spain and France dedicated to these scientific objectives during the period 1998 - 2000. Examples are presented of the use of multi- angular hyper-spectral measurements to determine LAI, fAPAR, cholorophyll and heat fluxes with both field and airborne measurements. Particular attention is dedicated to illustrate the need for multi-angular observations in the entire spectral range 0.4im - 14 im..
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Jose F. Moreno, Massimo Menenti, and Rolf Richter "Assessment of inputs to land surface processes models derived from hyperspectral multiangular data", Proc. SPIE 4542, Remote Sensing for Agriculture, Ecosystems, and Hydrology III, (28 January 2002);

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