Spatiotemporal Integration of Time-Series Remote Sensing and Soil Attributes for Precision Management Zoning in Daylily Cultivation

Effective management zone delineation is key to implementing site-specific strategies that address spatiotemporal heterogeneity in agriculture. Although time-series remote sensing offers a dynamic perspective, most current methods lack the framework to integrate it with soil properties, thereby hindering accurate characterization of crop growth variability. This study bridges the gap by developing a spatiotemporal framework that synthesizes remote sensing-derived phenology and soil attributes for daylily management zoning. Through a sequential approach—phenological metric extraction, SNIC-based segmentation, and STSF classification—we produce refined phenological time-series stacks. These outputs are designed to elucidate the drivers of field heterogeneity and directly inform precision management strategies. Compared to pixel-based and SNIC-based random forest, the STSF–SNIC framework increased spatial overlap rates by 5.4–8.0% (reaching 88.6%), despite comparable overall accuracy and kappa coefficients (OA/kappa: 92–94%). Geographical detector analysis identified village boundaries, soil type, total nitrogen, and organic carbon as key drivers of spatial patterns. A spatial generalized fuzzy c-means model, incorporating crop growth dynamics and soil gradients, reduced management zone fragmentation by 27.8% compared to conventional methods, with spatial autocorrelation analysis confirming enhanced spatial consistency (Moran’s I = 0.600 vs. 0.433, p < 0.001). In conclusion, by integrating time-series remote sensing phenology with soil attribute analysis within a spatially constrained clustering scheme, this study (1) provides a novel method for delineating coherent management zones, (2) reveals key drivers of crop growth heterogeneity, and (3) demonstrates a transferable pathway for translating satellite data into precision management actions. It thereby exemplifies the value of applied remote sensing in addressing practical challenges in sustainable agriculture.