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Estimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)  

Public Titre : Estimation of surface roughness over bare agricultural soil from Sentinel-1 data Type de document : Thèse/HDR Auteurs : Mohammad Choker, Auteur ; Nicolas Baghdadi, Directeur de thèse ; Mehrez Zribi, Directeur de thèse Editeur : Paris, Nancy, ... : AgroParisTech (2007 -) Année de publication : 2018 Importance : 214 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le grade de Docteur de l'Institut des Sciences et Industries du Vivant et de l'Environnement, AgroParisTech, Géomatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande C [Termes IGN] écho radar [Termes IGN] état de surface du sol [Termes IGN] humidité du sol [Termes IGN] image Cosmo-Skymed [Termes IGN] image Sentinel-SAR [Termes IGN] image TerraSAR-X [Termes IGN] modèle de rétrodiffusion [Termes IGN] polarisation [Termes IGN] rugosité du sol [Termes IGN] surface cultivée [Termes IGN] télédétection en hyperfréquence Index. décimale : THESE Thèses et HDR Résumé : (auteur) Spatial remote sensing is of paramount importance for mapping and monitoring environmental problems. Its interest lies in the ability of space satellite sensors in providing permanent information of the planet, at local, regional and global scales. Also, it provides spatial and repetitive territories visions and ecosystem views. Radar remote sensing has shown great potential in recent years for the characterization of soil surface conditions. The state of the soil surface, in particular moisture and roughness, has a fundamental influence on the distribution of rainfall between infiltration, surface retention and runoff. In addition, it plays an essential role in surface hydrological processes and those associated with erosion and evapotranspiration processes. Characterization and consideration of these surface conditions have been recently considered as an important issue for physically based modeling of hydrological processes or for surface-atmosphere coupling. In this context and for several years, several scientific studies have shown the potential of active microwave data for estimation of the soil moisture and the surface roughness.New SAR (Synthetic Aperture Radar) systems have opened new perspectives for earth observation through improved spatial resolution (metric on TerraSAR-X and COSMO-SkyMed) and temporal resolution (TerraSAR-X, COSMO-SkyMed, Sentinel-1) . The recent availability of new Sentinel-1 C-band radar sensors (free and open access) makes it essential to evaluate the potential of Sentinel-1 data for the characterization of soil surface conditions and in particular surface roughness.The work revolves around three parts. The first part consist of evaluation of the most used radar backscattering models (IEM, Oh, Dubois, and AIEM) using a wide dataset of SAR data and experimental soil measurements. This evaluation gives the ability to find the most robust backscattering model that simulates the radar signal with good agreement in order to use later in the inversion procedure of the radar signal for estimating the soil roughness. The second research axe of this thesis consists of proposing an empirical radar backscattering model for HH, HV and VV polarizations. This new model will be developed using a large real dataset. This new model also will be used in the inversion procedure of the radar signal for estimating the soil roughness. The last axe of this thesis consists of producing a method to invert the radar signal using neural networks. The objective is to evaluate the potential of Sentinel-1 data for estimating surface roughness. These neural networks will be trained using wide synthetic dataset produced from the radar backscattering models chosen (IEM calibrated by Baghdadi and the new proposed model) and validated using two datasets: one synthetic dataset and one real (Sentinel 1 images and in-situ measurements). The real datasets are collected from Tunisia (Kairouan) and France (Versailles). Note de contenu : 1- Introduction 2- Generalities 3- Evaluation of radar backscattering models 4- A new empirical model for radar scattering from bare soil surfaces 5- Estimation of soil roughness using neural networks from sentinel-1 SAR data 6- General conclusion and perspectives Numéro de notice : 25595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Paris : 2018 Organisme de stage : TETIS (Montpellier) nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02293194/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95218