###### Abstract  
The utilization of remote sensing technology to assess changes in crop net primary productivity (NPP), biomass, and carbon sequestration within the Fen River Basin, a crucial agricultural region in China, is important for achieving agricultural modernization, enhancing ecological environment quality, and obtaining carbon neutrality objectives. This study employed satellite remote sensing and the Carnegie-Ames-Stanford approach (CASA) model as research methods to investigate the temporal and spatial distribution characteristics of corn NPP in the Fen River Basin. Correlation analysis was conducted to examine the response of corn NPP to various environmental factors in the region, while aboveground biomass and carbon sequestration of corn were estimated using a biomass inversion model driven by NPP and principles of photosynthesis in green plants. The findings revealed that, from a temporal perspective, corn NPP in the Fen River Basin exhibited a unimodal variation pattern, with an average value of 368.65 gC/m\({}^{2}\). Spatially, the corn NPP displayed a discernible differentiation pattern, with the highest values primarily observed in the middle reaches of the Fen River Basin. Throughout the spatial and temporal variations in corn NPP during 2011-2020, the carbon sequestration capacity of corn exhibited an upward trend, particularly since 2017. The corn NPP displayed a positive correlation with temperature and precipitation. The response to solar radiation was mildly negative and a mildly positive correlation. In 2020, the aboveground biomass and carbon sequestration of corn followed a normal distribution, with the highest values concentrated in the northwestern part of the lower Fen River. CASA model; corn NPP; climatic factor; biomass; carbon sequestration amount 20241629420241629 Rapid advancements in remote sensing technology have contributed to their widespread application in the agricultural sector. Examples of its applications include monitoring and assessing soil nutrient levels in farmlands, tracking agricultural plant development, and evaluating agricultural output [3; 4]. The utilization of remote sensing technology in agriculture began with the launch of the Landsat Multispectral Scanning System (MSS) satellite in 1972, followed by a series of successive Landsat satellites (Landsat 2-8). These satellites have consistently provided high-quality images to assist land managers, researchers, and policymakers in the effective management of global natural resources [5]. In recent years, cutting-edge technologies such as remote sensing have emerged. These technologies hold significant promise for increasing crop yields, reducing input costs, and improving overall crop production efficiency [6; 7; 8]. Studies have demonstrated the potential of remote sensing images to generate high-resolution yield maps and assess yield variability in both field and landscape settings, enhancing the understanding of farmland's overall production potential [5; 9; 10; 11]. The common method for estimating grain yield, using remote sensing technologies, involves analyzing crop biochemical parameters through a simple statistical model that utilizes data from remote sensing images. This approach aims to extract crop yield information by establishing a transformation model between biochemical parameters and yields [12]. However, remote sensing yield estimation encounters challenges, including its limited applicability. Substantial variations in the study area, duration, and climatic conditions contributed to a significant error in the yield estimation. Furthermore, remote sensing data exclusively capture the dynamic aspects of the crop canopy, failing to comprehensively depict the underlying principles of crop growth or the impact of climatic conditions on its progress [13]. To enhance the precision and applicability of remote sensing yield estimation, a light energy utilization model such as the CASA model has been explored.