Abstract: Monitoring the spatiotemporal variation of soil geochemical background elements is crucial for regional ecological assessment, land resource management, and pollution risk control. Traditional monitoring methods struggle to balance spatial heterogeneity, dynamic changes, and cost-effectiveness, particularly in highly anthropogenically disturbed plain agricultural areas, leading to redundant or insufficient monitoring points that fail to capture critical environmental processes. This study focuses on a typical plain agricultural area in central Jiangsu Province, utilizing 1∶50000 soil geochemical survey data and integrating principal component analysis (PCA), Moran’s index, and semivariance functions to develop a unit monitoring strategy. The results show that PCA effectively decouples the dominant influences of natural background elements (Cr, Ni, Co, V) and human activity factors (As, Cd, Hg, Pb, N, P) on element distribution. Spatial autocorrelation analysis reveals that the range values of heavy metal elements (Cu, Cd, As) in the lakeside polder area (92.85–123.30km) are significantly larger than those of geological background elements (4.45–9.12km), with their spatial structure primarily driven by human activities. A dual-level spatial weight model based on PCA integrates soil geochemical element distribution with geological, soil, and geomorphological backgrounds, delineating six monitoring units. This study establishes a dynamic monitoring method that optimizes the spatiotemporal monitoring of soil geochemical background elements through composite weighting of comprehensive range parameters, addressing the inefficiencies of traditional methods in highly heterogeneous regions. The approach is applied to dynamic soil environmental quality monitoring and farmland management, providing parametric support for long-term monitoring network design in rapidly urbanizing regions.