###### Abstract
The CASA model is an ecological model relying on the photosynthetic efficiency of vegetation canopies. Through remote sensing technology, essential input parameters such as absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE) can be obtained, facilitating the estimation of net primary productivity (NPP) and biomass for regional-scale vegetation [14]. Gross primary productivity (GPP) represents the overall organic matter generated through primary production, considering both temporal and spatial dimensions. When deducting the total primary productivity from the material consumed by autotrophic respiration of the plant, the remaining organic matter is referred to as NPP. Vegetation NPP serves as a dependable metric for assessing the productive ability of vegetation communities and plays a pivotal role in evaluating carbon source/sink dynamics within ecosystems, as well as in regulating ecological processes [15]. In terms of crops, NPP is directly correlated with biological yield [16; 17]. In particular, in remote sensing yield estimation, different crop types exhibit variations in the NPP formation process, owing to their distinct growth and development characteristics. Therefore, estimating NPP for specific crops such as corn can facilitate crop yield estimation. [PERSON] et al. studied the NPP of corn from Iowa and Illinois in the United States in 2006, yielding an estimated result of 384 gC/m\({}^{2}\)[18]. In recent decades, numerous models employing remote sensing to estimate NPP have been developed. These models are effective in assessing temporal fluctuations in vegetation productivity affected by climate change and human activity. Examples include the Biom-BGC model, GLO-PEM model [14], MOD-Sim-Cycle model [19], and Carnegie-Ames-Stanford approach (CASA) model [20]. The CASA ecosystem model, often referred to as the light use efficiency (LUE) model, is widely utilized for predicting global NPP. It can successfully integrate spatial and temporal NPP patterns at the regional and global scales. The global success of the model can be attributed to the effective incorporation of remote sensing data at each pixel and its methodological simplicity. [PERSON] et al. [21] utilized the CASA model to investigate the spatiotemporal distribution of NPP in India's artificial forests, confirming the accuracy of the model's estimations. Currently, the global carbon cycle has garnered significant attention from governments, scientists, and the public. China, in particular, has taken a pioneering stance to achieve maximum carbon emission reduction by 2030 and ultimately attain zero net carbon emissions by 2060 [22]. Vegetation is a crucial part of the carbon sink in the carbon cycle of terrestrial ecosystems, actively absorbing CO\({}_{2}\) from the atmosphere through photosynthesis and effectively sequestering carbon [23]. In recent years, the carbon sequestration capacity of terrestrial vegetation has been extensively explored. [PERSON] et al. [24] focused on a deciduous maple forest in Tennessee and highlighted the role of nitrogen in constraining the impact of CO\({}_{2}\) fertilization on vegetation NPP. [PERSON] et al. [25] examined the impacts of land utilization, climate change, and CO\({}_{2}\) fertilization on plant primary productivity, carbon storage, and oxygen release capacity in the Tibetan Plateau. Their analysis employed 12 distinct ecosystem models to assess these dynamics comprehensively. [PERSON] and [PERSON] utilized an improved CASA model to estimate the NPP of winter wheat and summer maize in the Huanghuaihai region. Their findings indicated a positive correlation between NPP and solar radiation, while demonstrating a negative correlation between temperature and precipitation [26]. The purpose of this study was to use remote sensing and meteorological data by employing the CASA model to estimate corn NPP in the Fen River Basin.