《Quantifying the Impact of Vegetation on Carbon Monoxide Reduction Using Multi-Source Remote Sensing in China》

As climate change and environmental pollution intensify, 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 and failing to quantify the impacts of complex factors such as vegetation structure. This study integrates passive and active satellite remote sensing sensors with aerodynamic models to analyze the spatiotemporal distribution of vegetation's contribution to CO reduction across China from 2013 to 2022. Utilizing data from MODIS, Landsat, and the Gaofen series, we extend 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 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 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 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.