Multi-Source Indicator Modeling and Spatiotemporal Evolution of Spring Sowing Agricultural Risk Along the Great Wall Belt, China

The spatiotemporal heterogeneity of hydrothermal conditions during the spring sowing period profoundly shapes cropping layouts and sowing strategies. Using NASA’s GLDAS remote sensing reanalysis, we developed a continuous agricultural climate risk index that integrates three remotely driven indicators—spring sowing window days (SWDs) derived from a “continuous suitable-day” logic, the hydrothermal coordination degree (D value), and a comprehensive suitability index (SSH_SI)—thus advancing risk assessment from single metrics to a multidimensional framework. Methodologically, dominant periodic structures of spring sowing hydrothermal risk were extracted via a combination of wavelet power spectra and the global wavelet spectrum (GWS), while spatial trend-surface fitting and three-dimensional directional analysis captured spatial non-stationarity. The index’s spatial migration trajectories and centroid-evolution paths were then quantified. Results reveal pronounced gradients along the Great Wall Belt: SWD displays a “central-high, terminal-low” pattern, with sowing windows restricted to only 3–6 days in northeastern Inner Mongolia and western Liaoning but extending to 11–13 days in the central plains of Inner Mongolia and Shanxi; SSH_SI and D values form an overall “south-west high, north-east low” pattern, indicating more favorable hydrothermal coordination in southwestern areas. Temporally, although SWD and SSH_SI show no significant downward trend, their interannual variability has increased, signaling rising instability, whereas the D value declines markedly in most regions, reflecting intensified hydrothermal imbalance. The integrated risk index identifies high-risk hotspots in eastern Inner Mongolia and northern North China, and low-risk zones in western provinces such as Gansu and Ningxia. Centroid-shift analysis further uncovers a dynamic regional adjustment in optimal sowing patterns, offering scientific evidence for addressing spring sowing climate risks. These findings provide a theoretical foundation and decision support for optimizing regional cropping structures, issuing climate risk warnings, and precisely regulating spring sowing schedules.