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  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 11, No. 6 ( 2019-03-25), p. 715-
    In: Remote Sensing, MDPI AG, Vol. 11, No. 6 ( 2019-03-25), p. 715-
    Abstract: This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (EOS) (AMSR-E) sea surface temperature (SST) footprint associated with the remote sensing systems (RSS) level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each 4 × 4 km square contributing to the SST value of a given (AMSR-E) pixel. High-resolution L2 SST fields obtained from the moderate-resolution imaging spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution “ground truth” from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio ≈1.58, very close to the AMSR-E 6.93 GHz channel aspect ratio, ≈1.74. (The 6.93 GHz channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be aligned with the instantaneous field-of-view of the sensor as expected from the geometric characteristics of AMSR-E. Footprints were also analyzed year-by-year and as a function of latitude and found to be stable—no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminary analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained ignoring the shape and size of the footprint as has been the practice in such comparisons to date.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2513863-7
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  • 2
    Online Resource
    Online Resource
    American Meteorological Society ; 2016
    In:  Journal of Atmospheric and Oceanic Technology Vol. 33, No. 9 ( 2016-09), p. 1843-1858
    In: Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 33, No. 9 ( 2016-09), p. 1843-1858
    Abstract: Detailed understanding of submesoscale processes and their role in global ocean circulation is constrained, in part, by the lack of global observational datasets of sufficiently high resolution. Here, the potential of thermosalinograph (TSG) and Visible Infrared Imager Radiometer Suite (VIIRS) data is evaluated, to characterize the submesoscale structure of the near-surface temperature fields in the Gulf Stream and Sargasso Sea. In addition to spectral density, the structure function is considered, a statistical measure less susceptible to data gaps, which are common in the satellite-derived fields. The structure function is found to be an unreliable estimator, especially for steep spectral slopes, nominally between 2 and 3, typical of the Gulf Stream and Sargasso regions. A quality-control threshold is developed based on the number and size of gaps to ensure reliable spectral density estimates. Analysis of the impact of gaps in the VIIRS data on the spectra shows that both the number of missing values and the size of gaps affect the results, and that the steeper the spectral slope the more significant the impact. Furthermore, the TSG, with a nominal resolution of 75 m, captures the spectral characteristics of the fields in both regions down to scales substantially smaller than 1 km, while the VIIRS fields, with a nominal resolution of 750 m, reproduce the spectra well down to scales of about 20 km in the Sargasso Sea and 5 km in the Gulf Stream. The scales at which the VIIRS and TSG spectra diverge are thought to be determined by sensor and retrieval noise.
    Type of Medium: Online Resource
    ISSN: 0739-0572 , 1520-0426
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2016
    detail.hit.zdb_id: 2021720-1
    detail.hit.zdb_id: 48441-6
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  • 3
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2021
    In:  Journal of Geophysical Research: Oceans Vol. 126, No. 5 ( 2021-05)
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 126, No. 5 ( 2021-05)
    Abstract: The high density of vessel traffic and associated Automatic Identification System messages provide means to derive the oceanic surface current An Helmholtz‐Hodge decomposition of the optimally interpolated oceanic surface current field is performed In the core of the Greater Agulhas Current, analysis confirms that interpolated altimeter‐derived estimates largely underestimate actual surface current velocities
    Type of Medium: Online Resource
    ISSN: 2169-9275 , 2169-9291
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2021
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 161667-5
    detail.hit.zdb_id: 3094219-6
    SSG: 16,13
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  • 4
    Online Resource
    Online Resource
    MDPI AG ; 2017
    In:  Remote Sensing Vol. 9, No. 9 ( 2017-08-23), p. 877-
    In: Remote Sensing, MDPI AG, Vol. 9, No. 9 ( 2017-08-23), p. 877-
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2513863-7
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  • 5
    Online Resource
    Online Resource
    Societe Francaise de Photogrammetrie ; 2014
    In:  Revue Française de Photogrammétrie et de Télédétection , No. 202 ( 2014-04-16), p. 66-78
    In: Revue Française de Photogrammétrie et de Télédétection, Societe Francaise de Photogrammetrie, , No. 202 ( 2014-04-16), p. 66-78
    Abstract: Le développement des capteurs satellitaires d'observation des traceurs géophysiques à la surface de l'océan et les algorithmes de traitement associés ont connu un essor important au cours des vingt dernières années. Les différents capteurs satellitaires disponibles présentent des résolutions spatiales et temporelles différentes ainsi que différents niveaux de sensibilité à la couverture nuageuse. Dans le cas des images de température de surface de la mer, ceci se traduit notamment par de forts taux de données manquantes dans les observations de très haute-résolution (de l'ordre de 1kmx1km) contrairement aux observations de basse-résolution (de l'ordre de 25kmx25km). Il existe donc un enjeu fort pour exploiter conjointement les différentes sources d'information disponibles. Dans ce contexte, nous proposons un nouveau modèle stochastique de super-résolution basé sur une augmentation réaliste de l'information texturale des images. L'originalité de ce modèle réside dans la formulation d'a priori stochastiques sur la géométrie des images, qui sont caractéristiques des textures associées aux champs géophysiques à la surface de l'océan. Formellement, ce modèle consiste à modéliser les lignes de niveau de l'image comme des réalisations de marches aléatoires. Ce modèle stochastique s'étend naturellement à la simulation d'images haute-résolution à partir d'une observation basse-résolution. Cet article décrit la formulation mathématique du modèle proposé, ses caractéristiques théoriques ainsi que le schéma numérique mis en oeuvre pour la super-résolution d'images texturées. L'application à la simulation haute-résolution de champs de température de surface de la mer dans une région active de l'océan (courant des Aiguilles) démontre sa pertinence dans le contexte applicatif de la télédétection satellitaire de l'océan. Nous en discutons également les principales contributions ainsi que les différentes extensions possibles.
    Type of Medium: Online Resource
    ISSN: 2426-3974 , 1768-9791
    Language: Unknown
    Publisher: Societe Francaise de Photogrammetrie
    Publication Date: 2014
    detail.hit.zdb_id: 3099591-7
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