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Auteur Pedro Benevides |
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Mapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)
[article]
Titre : Mapping precipitable water vapor time series from Sentinel-1 interferometric SAR Type de document : Article/Communication Auteurs : Pedro Mateus, Auteur ; João Catalão, Auteur ; Giovanni Nico, Auteur ; Pedro Benevides, Auteur Année de publication : 2020 Article en page(s) : pp 1373 - 1379 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Appalaches
[Termes IGN] cartographie
[Termes IGN] données GNSS
[Termes IGN] image Sentinel-SAR
[Termes IGN] interferométrie différentielle
[Termes IGN] itération
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle atmosphérique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] phase GNSS
[Termes IGN] prévision météorologique
[Termes IGN] série temporelle
[Termes IGN] vapeur d'eauRésumé : (auteur) In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts. Numéro de notice : A2020-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946077 Date de publication en ligne : 28/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2946077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94672
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1373 - 1379[article]Analysis of Galileo and GPS integration for GNSS tomography / Pedro Benevides in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
[article]
Titre : Analysis of Galileo and GPS integration for GNSS tomography Type de document : Article/Communication Auteurs : Pedro Benevides, Auteur ; G. Nico, Auteur ; J. Catalão, Auteur Année de publication : 2017 Article en page(s) : pp 1936 - 1943 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] angle azimutal
[Termes IGN] atmosphère terrestre
[Termes IGN] données Galileo
[Termes IGN] humidité de l'air
[Termes IGN] intégration de données
[Termes IGN] Lisbonne
[Termes IGN] reconstruction 3D
[Termes IGN] réfraction atmosphérique
[Termes IGN] tomographie par GPSRésumé : (Auteur) Global Navigation Satellite System (GNSS) tomography provides 3-D reconstructions of atmosphere wet refractivity, related to water vapor. A simulated analysis of the integration of Global Positioning System and future Galileo data is presented. Atmospheric refractivity is derived from radiosonde data acquired over the Lisbon area. The impact of Galileo data on the tomographic reconstruction is assessed. Furthermore, horizontal anomalies are added to a reference vertical profile of atmospheric refractivity to reproduce low-level dry or wet air intrusions, a phenomenon commonly observed in meteorological data acquired by both radiosonde and satellites. The dependence of tomographic solution on the GNSS network density is also analyzed. Better reconstruction capabilities in the lower layers are observed when increasing the network density. Numéro de notice : A2017-170 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2631449 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2631449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84714
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 1936 - 1943[article]