Using satellite imagery to estimate CO2 partial pressure and exchange with the atmosphere in the Songhua River

Rivers play a vital role in the global carbon cycle, even though they account for only 0.58 % of the Earth's non-glaciated land surface area. Owing to the significant spatial and temporal variability of the partial pressure of CO2 (pCO2) in river water, there is a large degree of uncertainty in the estimation of CO2 flux from the river water-atmosphere interface. This study assembled more than 200 in-situ measured pCO2 in the mainstream water of Songhua River and matched with Sentinel-2 overpasses within ± 7 days from the GEE platform. Multiple linear regression (MLR) and two machine learning models (Random Forest and XGBoost) algorithms were used to estimate pCO2 in Songhua River. MLR, Random Forest, and XGBoost all demonstrated good performance and have the potential to map pCO2 for different years using high-quality Sentinel-2 images. In all the tested modeling approaches, the XGBoost model exhibited stable performance in the validation set (R2 > 0.79, RMSE < 228 µatm, MAPE < 42.93 %, SMAPE < 30.64 %). The distribution of mapped pCO2 by XGBoost model significantly varied with seasonal change, and the pCO2 in summer (445 ± 125 µatm) was higher than in autumn (373 ± 58 µatm) and spring (351 ± 57 µatm). CO2 emission flux from the mainstream water of Songhua River was calculated based on the inverse pCO2 value with a net annual uptake of 15.6 Gg C during the ice-free period. The findings suggest that the Songhua River functions as a carbon sink during both spring and autumn but as a carbon source during the summer. In addition, the potential control of annual CO2 flux spatial variability was attributed to the characteristics of the watershed landscapes. The study is critical to clarify the role of river systems in the global carbon cycle.