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Título: | Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity |
Autor: | Olmedo, Estrella CSIC ORCID ; Martínez, Justino CSIC ORCID ; Turiel, Antonio CSIC ORCID ; Ballabrera-Poy, Joaquim CSIC ORCID ; Portabella, Marcos CSIC ORCID | Palabras clave: | Soil Moisture and Ocean Salinity SMOS Sea surface salinity Salinity retrieval Remote sensing Physical oceanography SSS |
Fecha de publicación: | may-2017 | Editor: | Elsevier | Citación: | Remote Sensing of Environment 193: 103-126 (2017) | Resumen: | The Soil Moisture and Ocean Salinity (SMOS) mission has provided a unique remote sensing capability for observing key variables of the hydrological cycle, such as the Sea Surface Salinity (SSS). However, due to some limitations related to the instrument interferometric concept and its challenging data processing, SMOS SSS maps still display significant artifacts and biases, especially close to the coast, mainly due to the presence of Radio Frequency Interferences (RFI) and Land-sea contamination (LSC). In this paper, a new methodology for filtering salinity retrievals and correcting for spatial biases is introduced and validated | Descripción: | 24 pages, 18 figures, 5 tables | Versión del editor: | https://dx.doi.org/10.1016/j.rse.2017.02.023 | URI: | http://hdl.handle.net/10261/153277 | DOI: | 10.1016/j.rse.2017.02.023 | Identificadores: | doi: 10.1016/j.rse.2017.02.023 issn: 0034-4257 e-issn: 1879-0704 |
Aparece en las colecciones: | (ICM) Artículos |
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