2024-03-28T09:32:40Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1314162017-12-01T12:15:39Zcom_10261_123com_10261_8col_10261_376
urn:hdl:10261/131416
A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns
Galí, Martí
Devred, Emmanuel
Levasseur, Maurice
Royer, S.-J.
Babin, Marcel
Generalitat de Catalunya
Dimethylsulfoniopropionate
Sulfur cycle
Chlorophyll
Stratification
Particulate inorganic carbon
Algorithms
DMSP
PIC
14 pages, 9 figures, 2 tables, supplementary data http://dx.doi.org/10.1016/j.rse.2015.10.012
Dimethylsulfoniopropionate (DMSP) is a ubiquitous phytoplankton metabolite and the main precursor of the climate-active gas dimethylsulfide (DMS) in the oceans' surface. Here we use total DMSP (DMSPt) and ancillary measurements from a global database to develop a remote sensing algorithm for DMSPt in the upper mixed layer (UML). Over 55% of total DMSPt variability (log scale) is explained by in situ chlorophyll a (Chl) after dividing the database into two subsets, according to >stratified> and >mixed> water column criteria, based on the ratio between euphotic layer depth (Z) and mixed layer depth (MLD). Up to 70% of the variability is explained when adding sea surface temperature (SST) and log(Z/MLD) as predictors for the stratified and mixed subsets, respectively. Independent validation on satellite Chl match-ups indicates that the algorithm predicts DMSPt across three orders of magnitude with a root-mean-squared error spanning from 0.20 to 0.26 (log space) and mean absolute error typically around 45% (linear space). An additional submodel based on remotely sensed particulate inorganic carbon (PIC) is used to predict DMSPt in coccolithophore blooms if satellite Chl is not available. We use the algorithm to produce a monthly global DMSPt climatology, and estimate that DMSP synthesis amounts to 5-9% of oceanic phytoplankton gross carbon production. Our algorithm provides a new remote sensing tool for resolving temporal and spatial variations in DMSPt concentration, and represents a step forward toward improved diagnosis of contemporary DMS emission based on satellite Earth observation
2015-12
2016-04-25T12:48:43Z
artículo
Remote Sensing of Environment 171: 171-184 (2015)
http://hdl.handle.net/10261/131416
10.1016/j.rse.2015.10.012
http://dx.doi.org/10.13039/501100002809
eng
http://dx.doi.org/10.1016/j.rse.2015.10.012
Sí
closedAccess
Elsevier