Satellite-Based Estimation of Nitrous Oxide Concentration and Emission in a Large Estuary

Abstract

Estuaries are nitrous oxide (N₂O) emission hotspots and play an important role in the global N₂O budget. However, the large spatiotemporal variability of emission in complex estuary environments is challenging for large-scale monitoring and budget quantification. This study retrieved water environmental variables associated with N₂O cycling based on satellite imagery and developed a machine learning model for N₂O concentration estimations. The model was adopted in China’s Pearl River Estuary to assess spatiotemporal N₂O dynamics as well as annual total diffusive emissions between 2003 and 2022. Results showed significant variability in spatiotemporal N₂O concentrations and emissions. The annual total diffusive emission ranged from 0.76 to 1.09 Gg (0.95 Gg average) over the past two decades. Additionally, results showed significant seasonal variability with the highest contribution during spring (31 ± 3%) and lowest contribution during autumn (21 ± 1%). Meanwhile, emissions peaked at river outlets and decreased in an outward direction. Spatial hotspots contributed 43% of the total emission while covering 20% of the total area. Finally, SHapley Additive exPlanations (SHAP) was adopted, which showed that temperature and salinity, followed by dissolved inorganic nitrogen, were key input features influencing estuarine N₂O estimations. This study demonstrates the potential of remote sensing for the estimation of estuarine emission estimations.