###### Abstract  
Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (\(p<0.05\), \(R^{2}=0.59\)). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (\(p<0.01\), \(R^{2}=0.49\)). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting. 1  
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(including forests, shrubs, and grasslands, mainly forests) is the main source of carbon sequestered by terrestrial ecosystems [4]. The carbon sequestration capacity of this terrestrial vegetation (forests, shrubs, and grasslands) is usually expressed using productivity [5]. In order to achieve carbon neutrality, China will carry out several national ecological projects in the future [6]. Therefore, it is important to explore the factors influencing the spatial variability of vegetation carbon sequestration capacity (productivity) at the landscape scale for the future planning of the construction of national ecological projects. In recent years, biogeographers have become increasingly interested in the patterns of spatial variability in vegetation productivity and the factors that influence it [7]. In earlier studies, researchers used statistical models based on empirical and remote sensing data to construct productivity patterns in Chinese forests and found that forest productivity is the result of a combination of natural environmental factors and land-use patterns [8]. In subsequent studies, spatial variability in forest productivity was also commonly established in relation to indicators such as precipitation, temperature, elevation, and anthropogenic disturbances (fire, forest management, etc.) [5; 9; 10; 11]. Things in close proximity have similar geographic properties; hence, the spatial location of vegetation is a composite indicator that includes precipitation, temperature, and elevation (first law of geography) [12; 13]. However, few researchers have explored the relationship between the spatial variability of vegetation productivity and its spatial location. This may be related to the fact that the variability among vegetation in spatial location is more difficult to quantify using indicators. There have been studies on regional soil carbon loss and forest carbon dynamics considering the spatial location of the study object, and they selected some landscape indices as indicators to describe the spatial location of the study object [14; 15].