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| Picart, Stephane Saux; Tandeo, Pierre; Autret, Emmanuelle; Gausset, Blandine. |
| Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for... |
| Tipo: Text |
Palavras-chave: Machine learning; Systematic error; Sea surface temperature; Random forest. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00426/53797/54721.pdf |
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| Wang, Chen; Mouche, Alexis; Tandeo, Pierre; Stopa, Justin; Longépé, Nicolas; Erhard, Guillaume; Foster, Ralph C.; Vandemark, Douglas; Chapron, Bertrand. |
| The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed... |
| Tipo: Text |
Palavras-chave: Manual labelling; Ocean surface phenomena; Sentinel-1 wave mode; Synthetic aperture radar. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00512/62406/66659.pdf |
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| Tandeo, Pierre; Ailliot, Pierre; Autret, Emmanuelle. |
| Satellites provide important information on many meteorological and oceanographic variables. State-space models are commonly used to analyse such data sets with measurement errors. In this work, we propose to extend the usual linear and Gaussian state-space to analyse time series with irregular time sampling, such as the one obtained when keeping all the satellite observations available at some specific location. We discuss the parameter estimation using a method of moment and the method of maximum likelihood. Simulation results indicate that the method of moment leads to a computationally efficient and numerically robust estimation procedure suitable for initializing the Expectation-Maximisation algorithm, which is combined with a standard numerical... |
| Tipo: Text |
Palavras-chave: State-space model; Irregular sampling; Ornstein-Uhlenbeck process; EM algorithm; Sea surface temperature. |
| Ano: 2011 |
URL: http://archimer.ifremer.fr/doc/00039/15047/12441.pdf |
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| Tandeo, Pierre; Autret, Emmanuelle; Piolle, Jean-francois; Tournadre, Jean; Ailliot, Pierre. |
| The Advanced Along-Track Scanning Radiometer (AATSR) onboard Envisat is designed to provide very accurate measurements of sea surface temperature (SST). Using colocated in situ drifting buoys, a dynamical matchup database (MDB) is used to assess the AATSR-derived SST products more precisely. SST biases are then computed. Currently, Medspiration AATSR SST biases are discrete values and can introduce artificial discontinuities in AATSR level-2 SST fields. The new AATSR SST biases presented in this letter are continuous. They are computed, for nighttime and best proximity confidence data, by linear regression with different MDB covariables (wind speed, latitude, aerosol optical depth, etc.). As found, the difference between dual-view and nadir-only SST... |
| Tipo: Text |
Palavras-chave: Validation; Sea surface temperature (SST); Remote sensing; Advanced Along Track Scanning Radiometer (AATSR). |
| Ano: 2009 |
URL: http://archimer.ifremer.fr/doc/2009/publication-6135.pdf |
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| Martinez, Elodie; Brini, Anouar; Gorgues, Thomas; Drumetz, Lucas; Roussillon, Joana; Tandeo, Pierre; Maze, Guillaume; Fablet, Ronan. |
| Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific... |
| Tipo: Text |
Palavras-chave: Phytoplankton time-series reconstruction; Ocean color; Neural networks; Support vector regression; Multi-layer perceptron; Physical predictors. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00667/77871/80017.pdf |
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| Bentamy, Abderrahim; Grodsky, Semyon A; Cambon, Gildas; Tandeo, Pierre; Capet, Xavier; Roy, Claude; Herbette, Steven; Grouazel, Antoine. |
| More than twelve satellite scatterometers have operated since 1992 through the present, providing the main source of surface wind vector observations over global oceans. In this study, these scatterometer winds are used in combination with radiometers and synthetic aperture radars (SAR) for the better determination and characterization of high spatial and temporal resolution of regional surface wind parameters, including wind speed and direction, wind stress components, wind stress curl, and divergence. In this paper, a 27-year-long (1992–2018) 6-h satellite wind analysis with a spatial resolution of 0.125° in latitude and longitude is calculated using spatial structure functions derived from high-resolution SAR data. The main objective is to improve... |
| Tipo: Text |
Palavras-chave: Satellite scatterometer; Surface wind; Upwelling systems; Long time series. |
| Ano: 2021 |
URL: https://archimer.ifremer.fr/doc/00682/79416/81989.pdf |
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