Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery

Lakes are major emitters of methane (CH₄); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003–2020) and in situ measured data (2011–2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH₄ emissions and found that the random forest (RF) model achieved the best fitting accuracy (R² = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH₄ emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH₄ emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003–2020) daily time series of CH₄ emissions. This study demonstrates the great potential of satellites to map lake CH₄ emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.