Estimation of surface roughness over bare agricultural soil from Sentinel-1 data

Abstract : 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).
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Mohammad Choker. Estimation of surface roughness over bare agricultural soil from Sentinel-1 data. Computational Geometry [cs.CG]. AgroParisTech, 2018. English. ⟨NNT : 2018AGPT0001⟩. ⟨tel-02293194⟩

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