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Remote sensing of vegetation characteristics using 3d canopy architecture dynamic models


Research objectives and content
Remote sensing techniques are widely recognised as a useful tool for estimating vegetation biophysical characteristics and more generally monitoring canopies. Radiative transfer models are intensively used to describe vegetation processes and to interprete remote sensing data. The more recently developed explicitly account for the detailed canopy structure; these models lead to more accurate results but require as input a 3D description of canopy structure as realistic as possible. New satellites will sample the directional variation of the reflectance over a short time period during which canopy could possibly change. This temporal variation represents by itself a very interesting source of information and must be integrated in the interpretation algorithms.
This study aims at developing and evaluating a procedure that interpretes radiometric data using a data base made of 3D dynamic models of canopy structure and the corresponding simulated reflectance field. A parametric model of maize canopy architecture has been developed that mimics the time course of the vegetation and allows to simulate very realistic canopies using mainly plant vigor and its phenological stage as input. Radiatif transfer models will then be used to simulate the corresponding canopy reflectance. Canopy characteristics (vigor and state of development) will then be derived from the measuared temporal, directional and spectral reflectance, by comparison with the reflectance that was simulated previously with the data base. This study will eventually provide a more efficient way of using satellite data to monitor the vegetation. Training content (objective, benefit and expected impact)
This grant will allow the completion of a PhD in remote sensing. It will provide training in mathematics, physics, biology and computer techniques. Links with industry / industrial relevance (22)

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, domaine st paul
84914 Avignon

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EU contribution
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Participants (1)