## 1 Introduction
From an ecological perspective, carbon cycling processes and storage cycles in terrestrial ecosystems can also be calibrated based on biological productivity, which includes four parameters: gross primary productivity (GPP), net primary productivity (NPP), net ecosystem productivity (NEP), and net biome productivity (NBP) [31]. Among them, NEP is usually regarded as an indicator of terrestrial ecosystem carbon sources/sinks, and it is calculated by taking the gap between the carbon dioxide absorbed by vegetation through photosynthesis and the carbon consumed by vegetation autotrophic respiration (R\({}_{\rm{A}}\)) and soil microbial heterotrophic respiration (R\({}_{\rm{H}}\)), in which a positive NEP denotes a carbon sink and a negative NEP denotes a carbon source [32]. Therefore, terrestrial ecosystems' vegetation carbon sink can be evaluated using a model simulation approach. NPP and R\({}_{\rm{H}}\) are the two key factors in estimating NEP. The current models for estimating regional NPP fall into three types: statistical models, process-based models, and light use efficiency (LUE) models [33]. Among them, statistical models are simple to operate, but they are mainly based on climatic factors to construct models, which cannot deeply analyze the factors affecting biological productivity, especially the effects of land cover change due to human activities [34]. Process-based models estimate biological productivity by considering the physiological and ecological processes in plants, which helps to understand the interactions between vegetation and the environment, but their algorithms are more complex, have more input data, and are more computationally intensive, which makesit difficult to achieve large-scale biological productivity estimates with high spatial and temporal resolution [35]. LUE models are also well grounded in biophysics and are widely used due to their fewer parameters and higher accuracy of simulation. The Carnegie-Ames-Stanford Approach (CASA) is the most widely used representative of the LUE models [4]. However, CASA models are affected by varying input data sources and parameter localization, which may cause the estimates to be inaccurate in terms of local conditions [36], and therefore CASA models often need to be improved in the application process. Furthermore, current estimates of R\({}_{\rm H}\) are mainly made by constructing empirical models of R\({}_{\rm H}\) and its influencing factors [37; 38]. Among them, the model proposed by [PERSON] et al. can rapidly and accurately obtain the regional R\({}_{\rm H}\) by requiring only two factors: precipitation and NPP. Its results were validated by two datasets from around the world, and it was found to be better than other models that consider more factors [37]. Therefore, the main focus of this study includes (1) analyzing the number and spatial distribution of land cover changes in the ARNWC from 2001 to 2018; (2) estimating the vegetation carbon sources/sinks in the ARNWC from 2001 to 2018 using an improved CASA model and a soil microbial heterotrophic respiration model with NEP as an indicator of carbon source/sink and analyzing its spatial distribution and temporal variation characteristics; and (3) assessing the impact of land cover change on vegetation carbon source/sink. The findings of this study can (1) provide a reference for whether arid regions, especially desert ecosystems, are sinks or sources, (2) help explore the impact of land cover change on vegetation carbon sources/sinks in drylands, and (3) provide scientific guidance for subsequent environmental management efforts.