Spatial Patterns and Multiscale Driving Mechanisms of Cropland Vegetation in the Semiarid Transition Zone of the Loess Plateau

Unraveling the spatial differentiation and multiscale driving mechanisms of cropland vegetation in the Loess Plateau–Qinghai-Tibet Plateau transition zone provides vital scientific support for sustainable agriculture in ecologically fragile regions. Taking Yongjing County, Gansu Province as the study area, based on 30m resolution Enhanced Vegetation Index(EVI)from 2015 to 2024 and 9 topographic, hydrological, and climatic covariates, an integrated multi-model analytical framework coupling Geographical Detector (Geodetector), Random Forest with SHAP(SHapley Additive Explanations), Geographically Weighted Regression(GWR),and Variation Partitioning Analysis (VPA) was established. Furthermore, systematic robustness tests were conducted across four dimensions: vegetation index substitution, discretization schemes, spatial block sizes, and MGWR bandwidths. The results revealed that: (1) Cropland EVI exhibited significant moderate spatial positive autocorrelation(Moran’s I=0.494), with homogeneous spatial clustering being overwhelmingly dominant(95.8%); (2) Under the natural breaks discretization, the Topographic Wetness Index(TWI)yielded the highest explanatory power(q=0.200), but was highly sensitive to discretization methods (q dropped to 0.028 under quantile classification, a discrepancy of 0.177) and displayed an extremely narrow MGWR bandwidth(44 nearest neighbors), identifying it as a locally sensitive factor rather than a global governing factor; (3) Multi-model cross-validation and multidimensional robustness tests identified Digital Elevation Model(DEM),aspect cosine(Aspect_cos, northness), and mean annual temperature(Temp) as the three robust dominant factors. DEM ranked among the top three across all four methods with a discretization range of only 0.005,functioning as the global dominant factor orchestrating hydrothermal redistribution; Aspect_cos had the highest proportion of statistically significant pixels in GWR(77.4%), acting as a steady local dominant factor; Temp exhibited the greatest local coefficient variability(0.046), representing a spatially non-stationary dominant factor characterized by a pronounced north-south effect reversal. Overall, cropland vegetation in the transition zone is synergistically regulated by topography, hydrology, and climate, wherein topography governs macro-scale hydrothermal differentiation, while climatic drivers manifest intense spatial heterogeneity.