Recently, with the development of remote sensing technology, researchers have begun to use remote sensing data to analyze direct LCE [9]. For example, remote sensing technology can provide driving parameters for biogeochemical models (etc., BIOME-BGC model, CENTURY model), such as inputting precipitation, solar radiation, greenhouse, and other data, and simulate the carbon cycle impact of land use change on vegetation [15; 16; 17]. Currently, many studies on LCE have focused on the driving mechanism that affects LCE. For example, [PERSON], et al. [16] constructed a carbon emission model to estimate the total LCE in the Yellow River Delta of the Shandong Province of China and examine the driving factors of LCE changes. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [13] used the emission factor method to calculate the land use carbon sources and carbon sinks in Huainan City and identify the driving factors that influence the LCE based on the Logarithmic mean Divisia index (LMDI) model. [PERSON], et al. [18] analyzed the spatiotemporal changes in carbon emission intensity based on the nighttime light imagery and socioeconomic data in China and explored the relationship between social and economic development and carbon emission intensity. Moreover, previous studies often used the input-output model, grey relational analysis model, ordinary least squares model (OLS), and regression analysis to investigate the dominant factors of LCEs [19; 20; 21]. Notably, these methods ignored the difference in the actual spatial scale of various influence factors that impact the change in carbon emissions, which only revealed the relationship between LCE and the driving factors in a global dimension [9]. In addition, previous studies focused more on the carbon sink of terrestrial natural ecosystems, such as forests and grasslands, or the carbon emissions of urban energy consumption alone; using a combination of the two to analyze the overall net carbon emissions at the local scale is relatively rare, especially the analysis of the spatial heterogeneity of the driving factors of LCEs, which is still insufficient. Therefore, it is of great significance to the development of a low-carbon economy and to the formulation of carbon emission reduction policies designed to quantitatively assess the carbon emissions of different land use types from the spatial pixel scale and reveal the spatial heterogeneity of land carbon emissions in response to natural and human factors. The Guanzhong area (GZA) is located in the middle reaches of the Yellow River basin, which is an important economic zone in northwest China and an important ecological barrier [22]. With ecological protection and the promotion of high-quality developments in the Yellow River Basin presented as national and regional development strategies in 2019, sustainable development with carbon emission reduction as the goal has become an important breakthrough in the contradiction between protection and development [23]. However, the rapid development of urbanization and industrialization in the GZA has had a serious impact on the natural resources in recent years, such as the urban expansion, excessive energy consumption, grassland and forest exploitation, and other activities, which have aggravated the regional greenhouse effect [24]. Considering the backdrop of the carbon peak and carbon neutralization strategy, realizing carbon emission reduction is essential to creating regional high-quality developments in the GZA [25]. However, the current research on the carbon emissions of different land use types in the GZA is still insufficient. Therefore, the accurate estimation of LCE and the evaluation of its driving factors can provide a decision-making basis for carbon emission reduction policy formulation and low-carbon social and economic development in the GZA.