###### Abstract
In fact, the landscape index focuses on the spatial distribution and composition of ground objects, but it is difficult to quantify the relationship and variability among ground objects in terms of spatial location [16]. However, topological indicators derived from complex network theory can adequately represent the relative spatial relationships between ground objects (also called topological relationships between ground objects). Common topological indicators used to describe the topological characteristics of ground objects in the network are degree, clustering coefficient, centrality, etc. [17]. [PERSON] used a machine learning approach to screen indicators that have a strong influence on forest productivity from multiple observations of forests. The results revealed that topology is one of the important drivers of forest productivity [18]. However, the study did not go further in exploring which specific topological indicators influenced forest productivity. Our study continues to explore in this direction. Usually, a complex network representing vegetation topological characteristics needs to be constructed before calculating the topological indicators of vegetation. Complex network theory has been widely applied in ecology [19]. In studies related to landscape ecology, researchers often use an algorithm of complex networks to construct networks composed of vegetation or other types of landscape patches, which are called escapatial networks or ecological networks [20; 21]. In addition, the construction method of escapatial networks is very mature, mainly achieved by using various remote sensing data and MCR models [22]. The escapatial network consists of ecological corridors, ecological sources, and ecological nodes [22]. The escapatial network connects ecological nodes (extracted from the ecological source and representing landscape patches) through corridors to form a complete network system. Essentially, the escapatial network is an abstraction and simplification of the topology of landscape patches in space. At present, escapatial networks are mostly applied in studies of landscape ecological security, landscape risk assessment, and landscape heterogeneity and biodiversity. Few researchers have used the method in studies related to the spatial variability of vegetation productivity at the landscape scale. Regarding the derivation of vegetation productivity, statistical models based on empirical data have been used in more studies. Statistical models are relatively easy to operate and have some rationality [5; 7; 8; 23]. However, statistical models consider fewer factors, usually climatic factors such as temperature, precipitation, and solar radiation. In addition,they do not address biogeochemical processes in vegetated ecosystems. At the same time, the simulation results of most statistical models involve the potential vegetation productivity (the maximum productivity of vegetation that can be achieved when the climate is at its optimal state), rather than the real productivity of vegetation [24]. Compared with statistical models, biogeochemical models require different inputs of meteorological data, geographic information, and vegetation ecophysiological data at different locations, and they are able to simulate ecosystem processes in an exhaustive manner. Accordingly, the expansion of biogeochemical models at the spatial scale is more scientific and realistic [24]. Therefore, the classical biogeochemical model (also an ecosystem process model), Biome-BGC model, was used for the calculation of vegetation productivity in this study. The Yellow River Basin, located in the central region of China, is currently facing many ecological problems such as vegetation degradation, land desertification, and biodiversity reduction [25]. These ecological problems not only damage the human living environment, but also affect the growth of vegetation.