DMS-SAT_ALGORITHM

The repository DMS-SAT_ALGORITHM contains Matlab and R code to calculate total dimethylsulfoniopropionate (DMSP) and dissolved dimethylsulfide (DMS) in surface seawater using remote sensing and geophysical data. The DMS-SAT algorithm refers to the ensemble of the DMSP and DMS sub-algorithms as described in the following papers:

dmspt_sat.*

Galí, M., Devred, E., Levasseur, M., Royer, S. J., & Babin, M. (2015). A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns. Remote Sensing of Environment, 171, 171-184. https://doi.org/10.1016/j.rse.2015.10.012

dms_sat.*

Galí, M., Levasseur, M., Devred, E., Simó, R., & Babin, M. (2018). Sea-surface imethylsulfide (DMS) concentration from satellite data at global and regional scales. Biogeosciences, 15(11), 3497-3519. https://doi.org/10.5194/bg-15-3497-2018

OTHER ALGORITHMS, TESTING

I also provide here Matlab and R code for the following DMS algorithms, with which DMS-SAT shares some conceptual bases:

dms_sd02.*

Simó, R., & Dachs, J. (2002). Global ocean emission of dimethylsulfide predicted from biogeophysical data. Global Biogeochemical Cycles, 16(4), 26-1. https://doi.org/10.1029/2001GB001829

dms_vs07.*

Vallina, S. M., & Simó, R. (2007). Strong relationship between DMS and the solar radiation dose over the global surface ocean. Science, 315(5811), 506-508. https://doi.org:10.1126/science.1133680

test_sat_sd02_vs07.*

Test DMS-SAT (and the other algorithms) in a range of simple made-up cases.

LINKED DATASETS

Global DMS and DMSPt fields based on satellite data

This code repository is linked to the following zenodo dataset: https://doi.org/10.5281/zenodo.2558511. It contains global monthly DMS(P) climatologies produced with DMS-SAT, SD02 and VS07.

Sister repository: curated dataset used for DMS-SAT development and validation

The sister github repository DMS-SAT_DATA_DEV_VAL contains:

1. the curated dataset used to develop and validate DMS-SAT, consisting of the global sea-surface database from PMEL (https://saga.pmel.noaa.gov/dms/), quality controlled and extended with satellite and climatological data (as described by Galí et al. 2015 and 2018).
2. Scripts with useful data analysis tips. Can be used to reproduce the results from the Galí et al. 2015 and 2018 papers.