Assessing the complementarity of microwave and optical data for ecosystem modeling in arid regions

Eric Mougin; D. Lo Seen; Pierre Louis Frison; S. Rambal

doi:10.1117/12.200761

31 January 1995 Assessing the complementarity of microwave and optical data for ecosystem modeling in arid regions

Eric Mougin, D. Lo Seen, Pierre Louis Frison, S. Rambal

Proceedings Volume 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources; (1995) https://doi.org/10.1117/12.200761
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

A regional Sahelian grassland model has been designed with the objective that satellite data acquired in different regions of the electromagnetic spectrum can be used complementarily to drive the model. The evolution of the main processes and vegetation structural variables over a growing season are simulated by using standard meteorological data and a limited number of site-specific parameters. In order to analyze relationships between vegetation characteristics and satellite derived parameters, simulated vegetation variables are used as input parameters into physical models of reflectivity to compute VIS/NIR reflectances and radar backscattering coefficients. Reflectances corresponding to NOAA/AVHRR channels and NDVI profiles are simulated on a weekly basis for the entire growing season. Radar backscattering coefficients corresponding to the ERS-1 configuration are also computed. In both cases, area-weighted contributions from vegetation and bare soil are combined to compose the responses of heterogeneous surfaces. Simulated satellite data profiles are in good concordance with NOAA/AVHRR and ERS-1 wind scatterometer data. The complementarity of these two types of satellite for driving the grassland model data are then indicated.

Citation Download Citation

Eric Mougin, D. Lo Seen, Pierre Louis Frison, and S. Rambal "Assessing the complementarity of microwave and optical data for ecosystem modeling in arid regions", Proc. SPIE 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources, (31 January 1995); https://doi.org/10.1117/12.200761

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