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Seawater Debye model function at L-band and its impact on salinity retrieval from Aquarius satellite data / Yiwen Zhou in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8103 - 8116 Titre : Seawater Debye model function at L-band and its impact on salinity retrieval from Aquarius satellite data Type de document : Article/Communication Auteurs : Yiwen Zhou, Auteur ; Roger H. Lang, Auteur ; Emmanuel P. Dinnat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8103 - 8116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande L [Termes IGN] constante diélectrique [Termes IGN] eau de mer [Termes IGN] image SAC-D-Aquarius [Termes IGN] salinité [Termes IGN] température de surface de la mer Résumé : (auteur) A model function of seawater, which specifies the dielectric constant of seawater as a function of salinity, temperature, and frequency, is important for the retrieval of sea surface salinity using satellite data. In 2017, a model function has been developed based on measurement data at 1.4134 GHz using a third-order polynomial expression in salinity ( S ) and temperature ( T ). Although the model showed improvements in salinity retrieval, it had an inconsistent behavior between partitioned salinities. To improve the stability of the model, new dielectric measurements of seawater have been made recently over a broad range of salinities and temperatures to expand the data set used for developing the model function. The structure of the model function has been changed from a polynomial expansion in S and T to a physics-based model consisting of a Debye molecular resonance term plus a conductivity term. Each unknown parameter is expressed in S and T based on the expanded measurement data set. Physical arguments have been used to limit the number of unknown coefficients in these expressions to improve the stability of the model function. The new model function has been employed in the retrieval algorithm of the Aquarius satellite mission to obtain a global salinity map. The retrieved salinity using a different model function is compared with in situ data collected by Argo floats to evaluate the impact and the performance of model functions. The results indicate that the new model function has significant improvements in salinity retrieval compared with other existing models. Numéro de notice : A2021-708 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3045771 date de publication en ligne : 14/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3045771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98606 [article]

Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)  

Public [article]  inRemote sensing of environment > vol 258 (June 2021) . - n° 112385 Titre : Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing Type de document : Article/Communication Auteurs : Elliott White Jr, Auteur ; David Kaplan, Auteur Année de publication : 2021 Article en page(s) : n° 112385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] eau de mer [Termes IGN] Enhanced vegetation index [Termes IGN] Floride (Etats-Unis) [Termes IGN] littoral [Termes IGN] Louisiane (Etats-Unis) [Termes IGN] marais [Termes IGN] Mexique (golfe du) [Termes IGN] montée du niveau de la mer [Termes IGN] salinité [Termes IGN] série temporelle Résumé : (auteur) Coastal floodplain swamps (CFS) are an important part of the coastal wetland mosaic, however they are threatened due to accelerated rates of sea level rise and saltwater intrusion (SWI). While remote sensing-based detection of wholesale coastal ecosystem shifts (i.e., from forest to marsh) are relatively straightforward, assessments of chronic, low-level SWI into CFS using remote sensing have yet to be developed and can provide a critical early-warning signal of ecosystem deterioration. In this study, we developed nine ecologically-based hypotheses to test whether remote sensing data could be used to reliably detect the presence of CFS experiencing SWI. Hypotheses were motivated by field- and literature-based understanding of the phenological and vegetative dynamics of CFS experiencing SWI relative to unimpacted, control systems. Hypotheses were organized into two primary groups: those that analyzed differences in summary measures (e.g., median and distribution) between SWI-impacted and unimpacted control sites and those that examined timeseries trends (e.g., sign and magnitude of slope). The enhanced vegetation index (EVI) was used as a proxy for production/biomass and was generated using MODIS surface reflectance data spanning 2000 to 2018. Experimental sites (n = 8) were selected from an existing network of long-term monitoring sites and included 4 pairs of impacted/non-impacted CFS across the northern Gulf of Mexico from Texas to Florida. The four best-supported hypotheses (81% across all sties) all used summary statistics, indicating that there were significant differences in the EVI of CFS experiencing chronic, low-level SWI compared to controls. These hypotheses were tested using data across a large and diverse region, supporting their implementation by researchers and managers seeking to identify CFS undergoing the first phases of SWI. In contrast, hypotheses that assessed CFS change over time were poorly supported, likely due to the slow and variable pace of ecological change, relatively short remote sensing data record, and/or specific site histories. Overall, these results show that remote sensing data can be used to identify differences in CFS vegetation associated with long-term, low-level SWI, but further methodological advancements are needed to reliably detect the temporal transition process. Numéro de notice : A2021-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112385 date de publication en ligne : 12/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112385 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97851 [article]

Comparing the performance of turbulent kinetic energy and K-profile parameterization vertical parameterization schemes over the tropical indian ocean / Lokesh Kumar Pandey in Marine geodesy, vol 44 n° 1 (January 2021)  

Public [article]  inMarine geodesy > vol 44 n° 1 (January 2021) . - pp 42 - 69 Titre : Comparing the performance of turbulent kinetic energy and K-profile parameterization vertical parameterization schemes over the tropical indian ocean Type de document : Article/Communication Auteurs : Lokesh Kumar Pandey, Auteur ; Suneet Dwivedi, Auteur Année de publication : 2021 Article en page(s) : pp 42 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Océanographie [Termes IGN] Bengale, golfe du [Termes IGN] énergie cinétique [Termes IGN] Indien (océan) [Termes IGN] modélisation spatiale [Termes IGN] mousson [Termes IGN] salinité [Termes IGN] température de surface de la mer Résumé : (Auteur) The performance of vertical parameterization schemes, namely, turbulent kinetic energy (TKE) and K-profile parameterization (KPP), is evaluated over the domain [30E-120E; 20S-30N] in the Indian Ocean using the Nucleus for European Modeling of the Ocean (NEMO) regional model. The surface and sub-surface hydrography and mixed layer depth (MLD) of the simulations using TKE and KPP schemes have been compared. The KPP scheme produces higher bias (∼0.5 °C) of sea surface temperature (SST) in monsoon and post-monsoon seasons, which reduces on using the TKE scheme. The maximum surface salinity difference (0.45 psu) between TKE and KPP simulations is obtained over the head Bay of Bengal (BoB) in the post-monsoon months. The KPP scheme also overestimates MLD of the region. Barring highly convective regions as well as regions marked with very low and rapidly changing salinity, the TKE scheme performs better than KPP scheme in simulating the hydrography and MLD of the region. The differences between TKE and KPP simulations in the vertical stability and mixing are studied using buoyancy frequency, vertical shear of horizontal currents and energy required for mixing as quantifiers. The mixed layer heat budget analysis explains seasonal variability of SST and differences in vertical mixing parameterizations. Numéro de notice : A2021-059 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1835758 date de publication en ligne : 29/10/2020 En ligne : https://doi.org/10.1080/01490419.2020.1835758 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96849 [article]

The construction of sound speed field based on back propagation neural network in the global ocean / Junting Wang in Marine geodesy, vol 43 n° 6 (November 2020)  

Public [article]  inMarine geodesy > vol 43 n° 6 (November 2020) . - pp 621 - 642 Titre : The construction of sound speed field based on back propagation neural network in the global ocean Type de document : Article/Communication Auteurs : Junting Wang, Auteur ; Tianhe Xu, Auteur ; Wenfeng Nie, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 621 - 642 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] fonction orthogonale [Termes IGN] interpolation spatiale [Termes IGN] milieu marin [Termes IGN] onde acoustique [Termes IGN] propagation du son [Termes IGN] réseau neuronal artificiel [Termes IGN] salinité [Termes IGN] sondage acoustique [Termes IGN] température [Termes IGN] vitesse Résumé : (auteur) The sound speed is a key parameter that affects the underwater acoustic positioning and navigation. Aiming at the high-precision construction of sound speed field in the complex marine environment, this paper proposes a sound speed field model based on back propagation neural network (BPNN) by considering the correlation of learning samples. The method firstly uses measured ocean parameters to construct the temperature and salinity field. Then the spatial position, the temperature and the salinity information are used to construct the global ocean sound speed field based on the back propagation neural network algorithm. During the processing, the learning samples of back propagation neural network are selected based on the correlation between sound speed and distance. The proposed algorithm is validated by the global Argo data as well as compared with the spatial interpolation and the empirical orthogonal function (EOF) algorithm. The results demonstrate that the average root mean squares of the BPNN considering the correlation of learning samples is 0.352 m/s compared to the 1.527 m/s of EOF construction and the 2.661 m/s of spatial interpolation, with an improvement of 76.9% and 86.8%. Therefore, the proposed algorithm can improve the construction accuracy of sound speed field in the complex marine environment. Numéro de notice : A2020-694 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1815912 date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1080/01490419.2020.1815912 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96242 [article]

Using Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture / Ju Hyoung Lee in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 2 (February 2020) . - pp 91 - 98 Titre : Using Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture Type de document : Article/Communication Auteurs : Ju Hyoung Lee, Auteur Année de publication : 2020 Article en page(s) : pp 91 - 98 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique occidentale [Termes IGN] données multitemporelles [Termes IGN] erreur moyenne quadratique [Termes IGN] erreur systématique [Termes IGN] humidité du sol [Termes IGN] image SMOS [Termes IGN] salinité Résumé : (Auteur) To mitigate instantaneously evolving biases in satellite retrievals, a stochastic approach is applied over West Africa. This stochastic approach independently self-corrects Soil Moisture Ocean Salinity (SMOS) wet biases, unlike the cumulative density function (CDF) matching that rescales satellite retrievals with respect to several years of reference data. Ranked probability skill score (RPSS) is used as nonlocal root-mean-square errors (RMSEs) to assess stochastic retrievals. Stochastic method successfully decreases RMSEs from 0.146 m3/m3 to 0.056 m3/m3 in the Republic of Benin and from 0.080 m3/m3 to 0.038 m3/m3 in Niger, while the CDF matching method exacerbates the original SMOS biases up to 0.141 m3/m3 in Niger, and 0.120 m3/m3 in Benin. Unlike the CDF matching or European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA))–interim soil moisture, only a stochastic retrieval responds to Tropical Rainfall Measuring Mission rainfall. Based on the effects of bias correction, RPSS is suggested as a nonlocal verification without needing local measurements. Numéro de notice : A2020-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.2.91 date de publication en ligne : 01/02/2020 En ligne : https://doi.org/10.14358/PERS.86.2.91 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94772 [article]

On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state / Alexey Androsov in Journal of geodesy, vol 93 n° 2 (February 2019)  

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Sea surface salinity remote sensing / Emmanuel P. Dinnat (2019)  

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Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network / Walaiporn Phonphan in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)  

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Mapping a priori defined plant associations using remotely sensed vegetation characteristics / Hans D. Rölofsen in Remote sensing of environment, vol 140 (January 2014)  

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Multi sensor study of hydrological changes in Caspian Sea / Ayoub Moradi (sept 2014)

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The soil moisture and ocean salinity (SMOS) mission: first results and achievements / Yann H. Kerr in Revue Française de Photogrammétrie et de Télédétection, n° 200 (Novembre 2012)

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Overview of the first SMOS sea surface salinity products. Part 1: quality assessment for the second half of 2010 / N. Reul in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 1 (May 2012)  

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Validation of the SMOS L2 soil moisture data in the REMEDHUS network (Spain) / N. Sanchez in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 1 (May 2012)  

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Télédétection et salinité. Cartographie de la salinité des sols de la plaine algérienne du Bas-Chéliff / A. Douaoui in Géomatique expert, n° 76 (01/09/2010)

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Understanding sea-level rise and variability / J.A. Church (2010)

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