H
Hassan Bazzi
Researcher at University of Montpellier
Publications - 31
Citations - 705
Hassan Bazzi is an academic researcher from University of Montpellier. The author has contributed to research in topics: Computer science & Normalized Difference Vegetation Index. The author has an hindex of 8, co-authored 21 publications receiving 358 citations.
Papers
More filters
Journal ArticleDOI
Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas
TL;DR: The main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover, based on the synergic use of radar and optical data.
Journal ArticleDOI
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
Hassan Bazzi,Nicolas Baghdadi,Mohammad El Hajj,Mehrez Zribi,Dinh Ho Tong Minh,Emile Ndikumana,Dominique Courault,Hatem Belhouchette +7 more
TL;DR: This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France, providing a simple yet precise and powerful tool to map paddy rice areas.
Journal ArticleDOI
Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
TL;DR: The results showed that the C-band in VV polarization is able to penetrate the maize canopy even when the canopy is well developed due to high-order scattering along the soil-vegetation pathway that contains a soil contribution.
Journal ArticleDOI
Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
Hassan Bazzi,Nicolas Baghdadi,Dino Ienco,Mohammad El Hajj,Mehrez Zribi,Hatem Belhouchette,Maria Jose Escorihuela,Valérie Demarez +7 more
TL;DR: A method to map irrigated plots using S1 SAR (synthetic aperture radar) time series with high overall accuracy and the combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S 1 data.
Journal ArticleDOI
Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils
Nicolas Baghdadi,Mohammad El Hajj,Mohammad Choker,Mehrez Zribi,Hassan Bazzi,Emmanuelle Vaudour,Jean-Marc Gilliot,Dav M. Ebengo +7 more
TL;DR: In this paper, an inversion technique based on multi-layer perceptron neural networks is used to estimate the surface roughness (Hrms) over bare agricultural soils using the Sentinel-1 C-band SAR data in VV polarization.