The impact of landscape spatial morphology on green carbon sink in the urban riverfront area

The interaction between water and green spaces holds significant importance as an urban carbon sink, but there has been insufficient attention to how the specific morphology of waterfront landscapes affects their capacity for carbon sink. This study focuses on typical riverfront spaces in Shanghai, employing an improved Carnegie-Ames-Stanford-Approach (CASA) model fused with remote sensing spatiotemporal images to simulate vegetation fixed carbon within urban riverfront green spaces. Furthermore, an interpretable machine learning method was utilized to unveil the mechanism driving spatial heterogeneity in carbon sink efficiency. The results reveal the carbon sink efficiency of urban riverfront green spaces exhibits noticeable spatial heterogeneity, varying according to the location, type, scale, and river elements; The internal green component factors, including vegetation coverage and tree green ratio, along with surrounding environmental factor water surface ratio, are key factors influencing the carbon sinks efficiency; Hydrological elements within specific thresholds, namely, water surface ratio ranges between 0.245 and 0.281, can effectively enhance the carbon sink capacity of green spaces. And the maximum influencing value of distance from the water body is about 1800 m. The study contributes to developing a more scientific layout for climate-adaptive urban riverfront green spaces on the mesoscale.