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Publications about 'discrete scatterer radar model'

Articles in journal or book chapters

  1. Alireza Tabatabaeenejad, Mariko S. Burgin, and Mahta Moghaddam. Potential of L-Band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests. IEEE Transactions on Geoscience and Remote Sensing, 50(6):2150-2160, June 2012. Keyword(s): calibration, data analysis, forestry, remote sensing by radar, simulated annealing, soil, vegetation mapping, AD 2010 06, Jet Propulsion Laboratory, L-band radar, National Aeronautics and Space Administration, SMAP mission, Uninhabited Aerial Vehicle Synthetic Aperture Radar, absolute retrieval error analysis, calibration method, canopy parameter retrieval, central Canada boreal forests, discrete scatterer radar model, forest structure, forward scattering model, global optimization scheme, inversion method, old black spruce site, old jack pine forests, optimization algorithm, optimization problem, relative retrieval error, scattering mechanisms, simulated annealing, soil moisture information, subcanopy parameter retrieval, surface soil moisture retrieval, synthetic data, vegetation parameters, young jack pine forests, Backscatter, Dielectric constant, L-band, Radar, Scattering, Soil moisture, Vegetation mapping, Allometric relationships, Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10), L-band radar, Soil Moisture Active and Passive (SMAP), boreal forest, canopy, inverse problem, microwave scattering. [Abstract] [bibtex-entry]


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Please note that access to full text PDF versions of papers is restricted to the Chair of Earth Observation and Remote Sensing, Institute of Environmental Engineering, ETH Zurich.
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This collection of SAR literature is far from being complete.
It is rather a collection of papers which I store in my literature data base. Hence, the list of publications under PUBLICATIONS OF AUTHOR'S NAME should NOT be mistaken for a complete bibliography of that author.




Last modified: Mon Feb 1 16:39:36 2021
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .


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