Quantifying the impact of vegetation on carbon monoxide reduction using multi-source remote sensing in China

With the intensification of climate change and environmental pollution, reducing carbon monoxide (CO) emissions has become a focal point of global environmental governance. Existing research often relies on site-specific observational data, limiting continuous spatiotemporal analysis while failing to quantify the impacts of complex factors, such as vegetation structure. This study integrated passive and active satellite remote sensing sensors with aerodynamic models to analyze the spatiotemporal distribution of vegetation contributions to CO reduction across China from 2013 to 2022. This study extends traditional site-scale research to a continuous spatial scale, enhancing model accuracy through high-resolution satellite data. Our findings indicate that vegetation in regions east of the Hu-line primarily contributes to CO dry deposition, with a cumulative deposition of 5.2854 million tons, improving air quality by 0.00216% and yielding economic benefits of 7.436 billion USD. Forest-type vegetation contributes nearly ten times more to CO dry deposition than that of non-forest types, with significant spatiotemporal differences. Evergreen broadleaf trees contribute the most, with a cumulative reduction of 2.7826 million tons. Wavelet transform coherence analysis identifies the leaf area index (LAI) as the parameter with the highest coherence with CO dry deposition across all time-frequency scales, averaging a wavelet coherence of 0.79. The SHapley Additive exPlanations (SHAP) analysis indicates that temperature is the main factor influencing temporal fluctuations. These results provide a deeper understanding of vegetation's role in the global carbon cycle, offering significant implications for carbon reduction policies, vegetation management, and ecosystem service enhancement in China and globally.