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Soil roughness retrieval from TerraSar-X data using neural network and fractal method / Mohammad Maleki in Advances in space research, vol 64 n°5 (1 September 2019)  

Public [article]  inAdvances in space research > vol 64 n°5 (1 September 2019) . - pp 1117-1129 Titre : Soil roughness retrieval from TerraSar-X data using neural network and fractal method Type de document : Article/Communication Auteurs : Mohammad Maleki, Auteur ; Jalal Amini, Auteur ; Claudia Notarnicola, Auteur Année de publication : 2019 Article en page(s) : pp 1117-1129 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse fractale [Termes descripteurs IGN] bande X [Termes descripteurs IGN] équation intégrale [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] modèle d'inversion [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] rugosité du sol Résumé : (auteur) The purpose of this study is to estimate the surface roughness (rms) using TerraSar-X data in HH polarization. Simulation of data is carried out at a wide range of moisture and roughness using the Integral Equation Model (IEM). The inversion method is based on Multi-Layer Perceptron neural network. Inversion technique is performed in two steps. In the first step, the neural network is trained using synthetic data. The inputs of the first neural network are the backscattering coefficient and incidence angle, and the moisture is the output. In the next step, three neural networks are built based on a prior and without prior information on roughness. The inputs of three neural network are backscattering coefficient, estimated moisture in the first step and incidence angle and the roughness is output. The validation of the proposed methods is carried out based on synthetic and real data. Ground roughness measurements are extracted from Digital Terrain Model (DTM) using the fractal method. The accuracy of moisture from synthetic data is 6.1 vol% without prior information on moisture and roughness. The roughness (rms) accuracy of synthetic datasets is 0. 61 cm without prior information and is 0.31 cm and 0.38 cm for rms lower than 2 cm and rms between 2 and 4 cm, with prior information on roughness. The result's analysis of the simulated data showed that the prior information on roughness strongly improves the accuracy of roughness and moisture estimates. The accuracy of rms estimates for the TerraSar-X image in the HH polarization is about 0.9 cm in the case of no prior information on roughness. The accuracy improves to 0.57 cm for rms lower than 2 cm and 0.54 cm for rms between 2 and 4 cm with prior information on roughness. An overestimation of rms for rms lower than 2 cm and an underestimation of rms for rms higher than 2 cm are observed. The results of the accuracy of the synthetic and real data showed that the X band in HH polarization has a very good potential to estimate the soil roughness. Numéro de notice : A2019-411 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.04.019 date de publication en ligne : 24/04/2019 En ligne : https://doi.org/10.1016/j.asr.2019.04.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93527 [article]

Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth / Sébastien Labarre in Remote sensing of environment, vol 225 (May 2019)  

Public [article]  inRemote sensing of environment > vol 225 (May 2019) . - pp 1 - 15 Titre : Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth Type de document : Article/Communication Auteurs : Sébastien Labarre, Auteur ; Stéphane Jacquemoud, Auteur ; Cécile Ferrari, Auteur ; Arthur Delorme, Auteur ; Allan Derrien, Auteur ; Raphaël Grandin, Auteur ; Mohamed Jalludin, Auteur ; F. LemaÎtre, Auteur ; Marianne Metois, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Ewelina Rupnik , Auteur ; Bernard Tanguy, Auteur Année de publication : 2019 Projets : CAROLInA / Jacquemoud, Stéphane Article en page(s) : pp 1 - 15 Note générale : Bibliographie The PhD thesis of Sébastien Labarre was funded by the Direction générale de l'armement (DGA) and by the Commissariat à l'énergie atomique et aux énergies alternatives (CEA). Field data were acquired in the frame of the CAROLInA (Characterization of Multi-Scale Roughness using OpticaL ImAgery) project funded by CNES. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Djibouti [Termes descripteurs IGN] goniomètre [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] réflectance du sol [Termes descripteurs IGN] rugosité du sol [Termes descripteurs IGN] sol nu Résumé : (Auteur) Surface roughness can be defined as the mean slope angle integrated over all scales from the grain size to the local topography. It controls the energy balance of bare soils, in particular the angular distribution of scattered and emitted radiation. This provides clues to understand the intimate structure and evolution of planetary surfaces over ages. In this article we investigate the capacity of the Hapke photometric model, the most widely used in planetary science, to retrieve surface roughness from multiangular reflectance data. Its performance is still a question at issue and we lack validation experiments comparing model retrievals with ground measurements. To address this issue and to show the potentials and limits of the Hapke model, we compare the mean slope angle determined from very high resolution digital elevation models of volcanic and sedimentary terrains sampled in the Asal-Ghoubbet rift (Republic of Djibouti), to the photometric roughness estimated by model inversion on multiangular reflectance data measured on the ground (Chamelon field goniometer) and from space (Pleiades images). The agreement is good on moderately rough surfaces, in the domain of validity of the Hapke model, and poor on others. Numéro de notice : A2019-154 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.02.014 date de publication en ligne : 02/03/2019 En ligne : https://doi.org/10.1016/j.rse.2019.02.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92492 [article]

Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness / Yohei Sawada in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6195 - 6206 Titre : Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness Type de document : Article/Communication Auteurs : Yohei Sawada, Auteur ; Toshio Koike, Auteur ; Kentaro Aida, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 6195 - 6206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image Aqua-AMSR [Termes descripteurs IGN] image Aqua-MODIS [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar [Termes descripteurs IGN] rugosité du sol [Termes descripteurs IGN] teneur en eau de la végétation Résumé : (Auteur) Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted the performance of SSM (root-mean-square error reduced from 0.088 to 0.070) and VWC retrieval from the Advanced Microwave Scanning Radiometer 2 and Moderate Resolution Imaging Spectroradiometer. Since this surface soil roughness correction may be transferrable to other microwave satellite retrieval algorithms such as those for the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive satellites, this new algorithm can contribute to many microwave earth surface observation satellite missions. Numéro de notice : A2017-746 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2722468 En ligne : https://doi.org/10.1109/TGRS.2017.2722468 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88781 [article]

Global sensitivity analysis of the L-MEB model for retrieving soil moisture / Zengyan Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2949 - 2962 Titre : Global sensitivity analysis of the L-MEB model for retrieving soil moisture Type de document : Article/Communication Auteurs : Zengyan Wang, Auteur ; Tao Che, Auteur ; Yuei-An Liou, Auteur Année de publication : 2016 Article en page(s) : pp 2949 - 2962 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] bande L [Termes descripteurs IGN] densité de la végétation [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] rugosité du sol [Termes descripteurs IGN] température au sol Résumé : (Auteur) A global sensitivity analysis utilizing the extended Fourier amplitude sensitivity test is used to determine the parameter sensitivity of the L-band microwave emission of the biosphere (L-MEB) model. The results are analyzed from two perspectives of calibration and inversion. First, the parameters of surface soil moisture, soil roughness factor, vegetation optical depth at nadir, and effective land surface temperature are the four most sensitive parameters in the L-MEB model, demonstrating their possibility to be retrieved in the multiparameter retrieval approaches. Then, the high total sensitivity index (TSI) values of surface soil temperature in the analyses emphasize the importance of high-precision land surface temperature data in the surface soil moisture retrievals, especially for rougher or more vegetated surface conditions. Finally, our analysis indicates that TSI values are high for the soil surface roughness and vegetation optical depth model parameters but low for the vegetation structure, single scattering albedo, and soil roughness coefficient model parameters at incidence angles near nadir. This suggests that calibration experiments performed at small incidence angles may be appropriate for some but not all of the model parameters, which characterize the effect of soil surface roughness and vegetation on the terrestrial brightness temperature. Consequently, new calibration procedures that account for the different relative sensitivities of these model parameters at larger incidence angles may need to be developed in the future. Numéro de notice : A2016-847 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2509176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82928 [article]

LiDAR-derived surface roughness texture mapping: Application to mount St. Helens Pumice Plain deposit analysis / Patrick L. Whelley in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 2 (January 2014) . - pp 426 - 438 Titre : LiDAR-derived surface roughness texture mapping: Application to mount St. Helens Pumice Plain deposit analysis Type de document : Article/Communication Auteurs : Patrick L. Whelley, Auteur Année de publication : 2014 Article en page(s) : pp 426 - 438 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] alluvion [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] montagne [Termes descripteurs IGN] plaine [Termes descripteurs IGN] rugosité du sol [Termes descripteurs IGN] Saint-Helens, Mont [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] volcan Résumé : (Auteur) Statistical measures of patterns (textures) in surface roughness are used to quantitatively differentiate volcanic deposit facies on the Pumice Plain, on the northern flank of Mount St. Helens (MSH). Surface roughness values are derived from a Light Detection and Ranging (LiDAR) point cloud collected in 2004 from a fixed-wing airborne platform. Patterns in surface roughness are characterized using co-occurrence texture statistics. Pristine-pyroclastic, reworked-pyroclastic, mudflow, boulder beds, eroded lava flows, braided streams, and other units within the Pumice Plain are all found to have significantly distinct roughness textures. The MSH deposits are reasonably accessible, and the textural variations have been verified in the field. Results of this work indicate that by affecting the distribution of large clasts and tens-of-meter scale landforms, modification of pyroclastic deposits by lahars alters the morphology of the surface in detectable quantifiable ways. When a lahar erodes a pyroclastic deposit, surface roughness increases, as does the randomness in the deposit surface. Conversely, when a lahar deposits material, the resulting landforms are less rough but more random than pristine pumice-rich pyroclastic deposits. By mapping these relationships and others, volcanic deposit facies can be differentiated. This new method of mapping, based on roughness texture, has the potential to aid mapping efforts in more remote regions, both on this planet and elsewhere in the solar system. Numéro de notice : A2014-040 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32945 [article]

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