Remote sensing of dissolved CO2 concentrations in meso-eutrophic lakes using Sentinel-3 imagery

Satellite observation can significantly reduce the uncertainties in CO2 emission estimations compared to insuf-ficient field data. However, big challenges remain in developing remote sensing-based models for mapping concentrations of dissolved carbon dioxide (cCO2) in lakes at regional or global scales. We developed a cCO2 estimation model using Sentinel-3-derived lake environmental variables and near-synchronous field cCO2 data from 16 lakes in the middle and lower reaches of the Yangtze and Huai River (ML_YHR) basins in Eastern China (N = 248). Stepwise quadratic polynomial regressions of several combinations of chlorophyll -a (Chl-a), water temperature (Tw), Secchi disk depth (ZSD), and photosynthetic active radiation (PAR)-related variables were tested and validated to select the best approach. The final model showed high performance in calibration and validation (R2 > 0.72, RMSE < 6.35 mu mol L-1, MAPE < 30.31%). The model sensitivity analysis, based on Monte Carlo simulations, showed the model's estimated bias as <25% based on uncertainties of all input variables. Spatial and temporal dynamics of dissolved CO2 concentrations in 113 lakes (>= 10 km2) in the ML_YHR basins were mapped from 2016 to 2021 using the Sentinel-3 data. The result showed that CO2 concentrations were low in the summer and autumn but high in the winter and spring with dramatic variations (e.g., mean coefficient of variation: 52.59%). The annual mean CO2 concentrations of lakes revealed that about 28% of the lakes acted as weak atmospheric CO2 sinks (14.96 +/- 1.13 mu mol L-1) while the rest were sources (19.22 +/- 2.02 mu mol L-1), compared with a mean concentration of CO2 atmospheric equilibrium (16.29 mu mol L-1). CO2 concentrations decreased with increasing eutrophication and decreasing lake size (p < 0.05). This study advances current knowledge about CO2 emissions from emerging meso-eutrophic lakes and shows how satellite remote sensing can expand the spatiotemporal coverage of lake CO2.

Automated summary

Satellite observations can effectively reduce uncertainties in CO2 emission estimations compared to insufficient field data. However, developing remote sensing-based models for mapping cCO2 concentrations in lakes at regional or global scales remains a significant challenge. In this study, we developed a cCO2 estimation model using Sentinel-3-derived lake environmental variables and field cCO2 data from 16 lakes in Eastern China. The model showed high performance in calibration and validation, and the spatial and temporal dynamics of dissolved CO2 concentrations in 113 lakes were successfully mapped using Sentinel-3 data. The study provides important insights into CO2 emissions from meso-eutrophic lakes and highlights the potential of satellite remote sensing in expanding the coverage of lake CO2.