A Multi-Mode Convolutional Neural Network (CNN_MM) to reconstruct satellite-derived Chlorophyll-a time series in the global ocean from physical drivers

This repository contains the code of the model presented in the paper [A Multi-Mode Convolutional Neural Network to reconstruct Chlorophyll-a time series in the global ocean from physical drivers](Frontiers in Marine Science, 2023).

Contents

This repository contains the following PyTorch code:

• Implementation of the multi-mode CNN_MM8 Chl time series regression from oceanic and atmospheric predictors :

Results

Our model achieves the following performance on INDIGO Benchmark dataset :

Model name	r2	RMSE	Slope	Seas	Inter	N param	Time computation	Km travelled by car
CNNMM8	0.87	0.28	0.90	1.00	0.96	803 920	39 h	8.9

See the paper for more details.

Requirements

Python Libraries :

• torch==1.4.0
• torchvision==0.5.0
• numpy==1.18.1
• carbontracker==1.1.5

INDIGO dataset download :

The benchmark formated dataset is available for download here. You can also find the required files to run the code in this repository. This dataset was built from the following source of data :

Proxy used as predictors	Acronyme	Products	Initial spatio-temporal resolutions
Sea Surface Temperature	SST	Reyn_SmithOIv2 SST dataset	Monthly on a 1◦ × 1◦ spatial grid
Sea Level Anomaly	SLA	Ssalto/Duacs merged product of CNES/SALP project	Weekly on a 1/3◦ × 1/3◦ spatial grid
Zonal and Meridional surface winds	Uera, Vera	Atmospheric model reanalysis ERA interim 4	Every 5-days on a 0.25◦ × 0.25◦ spatial grid
Zonal and Meridional surface total currents	u,v	OSCAR unfiltered satellite product	Every 5-days on a 0.25◦ × 0.25◦ spatial grid
Short-wave radiations	SW	NCEP/NCAR Numerical reanalysis	Daily on a 2◦ grid
Binary continental mask	mask
Bathymetry	bathy	GEBCO	15 arc seconds

Animation

Chl recontructed data over [2012-2015] :

animation_Chl_reconstructed_2012_2015.mov

Chl reference satellite data over [2012-2015] :

animation_Chl_satellite_reference_2012_2015.mov

References

Roussillon Joana, Fablet Ronan, Gorgues Thomas, Drumetz Lucas, Littaye Jean, Martinez Elodie (2023). A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived Chlorophyll-a time series in the global ocean from physical drivers. Frontiers in marine science. doi: 10.3389/fmars.2023.1077623

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