A5042819729- Marine and coastal ecosystems
- Oceanographic and Atmospheric Processes
- Marine Biology and Ecology Research
- Aeolian processes and effects
- Remote Sensing in Agriculture
- Water Quality Monitoring and Analysis
- Land Use and Ecosystem Services
- Remote Sensing and LiDAR Applications
- Hydrological Forecasting Using AI
- Water Quality Monitoring Technologies
- Climate variability and models
- Soil erosion and sediment transport
- Tree Root and Stability Studies
- Geology and Paleoclimatology Research
- Marine and environmental studies
- Geochemistry and Geologic Mapping
- Data Analysis with R
Ifremer
2020-2024
Laboratory for Ocean Physics and Satellite Remote Sensing
2020-2024
Centre National de la Recherche Scientifique
2020-2024
Institut de Recherche pour le Développement
2020-2024
Institut Universitaire Européen de la Mer
2020-2024
Géosciences Environnement Toulouse
2024
Université de Bretagne Occidentale
2024
Laboratoire d’Études en Géophysique et Océanographie Spatiales
2023
Laboratoire Interuniversitaire des Systèmes Atmosphériques
2018
École Supérieure des Géomètres et Topographes
2015
Time series of satellite-derived chlorophyll-a concentration (Chl, a proxy phytoplankton biomass), continuously generated since 1997, are still too short to investigate the low-frequency variability biomass (e.g. decadal variability). Machine learning models such as Support Vector Regression (SVR) or Multi-Layer Perceptron (MLP) have recently proven be an alternative approach mechanistic ones reconstruct Chl synoptic past time-series before satellite era from physical predictors....
Phytoplankton plays a key role in the carbon cycle and supports oceanic food web. While its seasonal interannual cycles are rather well characterized owing to modern satellite ocean color era, longer time variability remains largely unknown due short time-period covered by observations on global scale. With aim of reconstructing this longer-term phytoplankton variability, support vector regression (SVR) approach was recently considered derive surface Chlorophyll-a concentration (Chl, proxy...
Abstract Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐ a concentration (Schl), proxy for phytoplankton biomass, using six of most widely used merged satellite products. Significant regional variations are observed, with contrasting observed among different To assess if these can be related to changes environment or bias radiometric products, convolutional neural network is examine relationship between...
Phytoplankton sustains marine ecosystems and influences global carbon dioxide levels through photosynthesis. To grow, phytoplankton rely on nutrient availability in the upper sunlit layer, closely related to ocean dynamics specifically stratification. Human-caused climate change is responsible, among others, for an increase temperature regional modifications of winds, thus affecting stratification ocean's surface. Consequently, biomass expected be impacted by these environmental changes....
<p>Phytoplankton plays a key role in the carbon cycle and fuels marine food webs. Its seasonal interannual variations are relatively well-known at global scale thanks to satellite ocean color observations that have been continuously acquired since 1997. However, satellite-derived chlorophyll-a concentrations (Chl-a, proxy of phytoplankton biomass) time series still too short investigate biomass low-frequency variability. Machine learning models such as support vector regression...
The authors wish to make the following corrections paper [...]
<p>Phytoplankton plays a key role in the carbon cycle and constitutes basis of marine food web. Its seasonal interannual cycles are relatively well-known on global scale thanks to continuous ocean color satellite observations acquired since 1997. The satellite-derived chlorophyll-a concentrations (Chl-a, proxy phytoplankton biomass) time series still too short investigate biomass low-frequency variability. However, it is vital prerequisite before being able confidently detect...