Next Article in Journal
Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales
Next Article in Special Issue
Modelling the Visibility of Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea
Previous Article in Journal
Effects of the COVID-19 Lockdown on Urban Light Emissions: Ground and Satellite Comparison
Previous Article in Special Issue
Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
Article

Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea

1
Department of Earth Sciences, Uppsala University, SE-752 36 Uppsala, Sweden
2
Brockmann Geomatics Sweden AB, SE-164 40 Kista, Sweden
3
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 259; https://doi.org/10.3390/rs13020259
Received: 19 November 2020 / Revised: 25 December 2020 / Accepted: 12 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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 (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 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 pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), 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 pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 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 pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea. View Full-Text
Keywords: pCO2; remote sensing; random forest; variable importance; the Baltic Sea pCO2; remote sensing; random forest; variable importance; the Baltic Sea
Show Figures

Graphical abstract

MDPI and ACS Style

Zhang, S.; Rutgersson, A.; Philipson, P.; Wallin, M.B. Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea. Remote Sens. 2021, 13, 259. https://doi.org/10.3390/rs13020259

AMA Style

Zhang S, Rutgersson A, Philipson P, Wallin MB. Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea. Remote Sensing. 2021; 13(2):259. https://doi.org/10.3390/rs13020259

Chicago/Turabian Style

Zhang, Shuping, Anna Rutgersson, Petra Philipson, and Marcus B. Wallin. 2021. "Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea" Remote Sensing 13, no. 2: 259. https://doi.org/10.3390/rs13020259

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop