Abstract

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO(2)) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO(2) estimation in the Baltic Sea and derived monthly pCO(2) maps for the marginal sea during the period of July 2002-October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO(2) estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO(2) estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (a(CDOM)), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO(2) estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO(2)-relevant variables (e.g., a(CDOM)), particularly in the summer months. In addition, the variables' importance for pCO(2) estimation varied between seasons and sub-basins. For example, the importance of a(CDOM) were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO(2) estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 mu atm. The pCO(2) maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO(2) in the Baltic Sea. The spatially and seasonally varying variables' importance for the pCO(2) estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO(2) estimation in marginal seas using remote sensing techniques. The pCO(2) maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

Keywords

pCO(2); remote sensing; random forest; variable importance; the Baltic Sea

Published in

Remote Sensing
2021, Volume: 13, number: 2, article number: 259
Publisher: MDPI

SLU Authors

• Wallin, Marcus

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    
  • Uppsala University

UKÄ Subject classification

Remote Sensing


Publication identifier

DOI: https://doi.org/10.3390/rs13020259

Permanent link to this page (URI)

https://res.slu.se/id/publ/110699