seanoe:91910 data@seanoe.org 2023-03-24 ISO 19115:2003/19139 1.0 satellIte phytoplaNkton Drivers In the Global Ocean over 1998-2015 (INDIGO Benchmark dataset) 2022-11 2023-03-24 Roussillon Joana, Fablet Ronan, Gorgues Thomas, Drumetz Lucas, Littaye Jean, Martinez Elodie (2022). satellIte phytoplaNkton Drivers In the Global Ocean over 1998-2015 (INDIGO Benchmark dataset). SEANOE. https://doi.org/10.17882/91910 This benchmark dataset contains the physical data used as predictors to reconstruct global chlorophyll-a concentrations (Chl, a proxy of phytoplankton biomass) in Roussillon et al., as well as the reference satellite Chl target fields. The nine physical predictors' data (Short-Wave radiations, Sea Surface Temperature, Sea Level Anomaly, Zonal and meridional surface currents, Zonal and meridional surface wind stress, Bathymetry, Binary continental mask) were extracted from publicly available datasets over [1998-2015] and resampled to the same spatio-temporel resolution as Chl, i.e. monthly on a 1°x1° grid between 50°N and 50°S. Missing values were gap-filled using the heat diffusion equation. Each variable was normalized by substracting its mean from the original values and dividing by its standard deviation over [1998-2015]. This dataset was used to train and validate the Multi-Mode Convolutional Neural network (CNNMM8) introduced in Roussillon et al. ; reconstructed monthly Chl fields over the [2012-2015] test period are also provided here. We hope this benchmark dataset can help to promote the improvements of methods as well as the emergence of new ideas, as building datasets is sometimes more time-consuming than the implementation of machine learning tools themselves. This would also facilitate the quantitative comparison of models performances' on the exact same datasets. Roussillon Joana UMR6523 Laboratoire d'Oceanographie Physique et Spatiale (LOPS), France Fablet Ronan IMT Atlantique, Lab-STICC, UMR CNRS 6285, France Gorgues Thomas UMR6523 Laboratoire d'Oceanographie Physique et Spatiale (LOPS), France Drumetz Lucas IMT Atlantique, Lab-STICC, UMR CNRS 6285, France Littaye Jean UMR6523 Laboratoire d'Oceanographie Physique et Spatiale (LOPS), France Martinez Elodie UMR6523 Laboratoire d'Oceanographie Physique et Spatiale (LOPS), France SEANOE https://www.seanoe.org/data/00807/91910/illustration.gif phytoplankton physical drivers satellite ocean color time-series regression global scale deep learning benchmark Biological oceanography Physical oceanography Ocean colour Phytoplankton Pigments ODATIS aggregation parameters and Essential Variable names 2024-09-16 geonetwork.thesaurus.external.theme.NVS.OD1 Biological oceanography Physical oceanography SeaDataNet Parameter Disciplines 2021-04-07 geonetwork.thesaurus.external.parameter.NVS.P08 Aggregate data Type de jeux de donnée ODATIS 2024-10-15 geonetwork.thesaurus.local.theme.type_jeux_donnee CC-BY (Creative Commons - Attribution) A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers 2023 Frontiers Media SA ROUSSILLON JOANA FABLET RONAN GORGUES THOMAS DRUMETZ LUCAS LITTAYE JEAN MARTINEZ ELODIE 10.3389/fmars.2023.1077623 oceans 180.0 -180.0 -50.0 50.0 1998-01-01 2015-12-31 NUMPY ARRAY https://www.seanoe.org/data/00807/91910/data/97898.npy WWW:DOWNLOAD-1.0-link--download Processed data Normalized physical input data over 1998-2015 - 945 MB https://www.seanoe.org/data/00807/91910/data/97902.npy WWW:DOWNLOAD-1.0-link--download Processed data Reference target satellite Chl over 1998-2015 - 105 MB https://www.seanoe.org/data/00807/91910/data/97925.npy WWW:DOWNLOAD-1.0-link--download Processed data Reconstructed Chl over 2012-2015 test period - 23 MB https://doi.org/10.17882/91910 WWW:LINK-1.0-http--metadata-URL DOI of the product https://www.seanoe.org/data/00807/91910/ rel-canonical Seanoe