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.
Han et al. (Fri,) studied this question.