Associations Between Cropland Spatial Patterns and Environment-Adjusted Vegetation Greenness in Luliang County, China

Cropland transition involves not only changes in land quantity but also spatial restructuring and shifts in vegetation functioning. Taking Luliang County, China, as a case study, this study integrated multi-temporal land-use data from 1980 to 2020 with normalized difference vegetation index (NDVI), soil, topographic, hydrological, climatic, and socioeconomic datasets. Cropland structure was characterized by quantity, configuration, and cropland–built-up land adjacency intensity (CAI). A generalized additive model (GAM) was used to construct a resource–environment baseline for expected cropland NDVI, while spatial block cross-validation was used to derive out-of-sample functional deviations. Lag-augmented models and models augmented with historical structural information were further applied to assess temporal dependence, and generalized additive mixed models were used to examine nonlinear structure–function relationships under different development pressures. From 1980 to 2020, cropland area declined by only 2.70%, whereas fragmentation, boundary complexity, and cropland–built-up land adjacency generally increased, indicating substantial internal restructuring. Although observed NDVI partially recovered after 2010, the point estimate of mean environment-adjusted NDVI deviation decreased, and negative-deviation states persisted between sampled years. Both antecedent and historical cropland structures provided additional predictive information beyond contemporary structure and improved out-of-block prediction across spatial scales. Nonlinear models outperformed linear specifications, and the fitted CAI–NDVI deviation relationship declined at high adjacency intensity under the population-density conditions examined, with a candidate structural range of approximately 5.1–5.4 km km−2 at the 1 km analytical scale. These findings indicate that cropland vulnerability cannot be adequately assessed from area change or absolute vegetation greenness alone, and highlight the importance of integrating spatial restructuring, environmental expectations, temporal structural information, and population-conditioned interface associations.