A novel framework for bridging cropland morphological structure and ecological quality using a remote sensing-based continuous change detection model

Understanding how cropland morphology influences ecological quality is essential for sustaining agricultural development, but these two dimensions are often studied independently. This study develops an integrated framework that links cropland morphological evolution with ecological quality by extending the landscape ecology “pattern–process–function” theory to agricultural systems. Using time-series Landsat data, we applied a continuous change detection model to generate annual cropland maps, classify morphological structures (core, perforated, edge, scattered), and encode pixel-level transitions to track evolutionary pathways. A Comprehensive Ecological Evaluation Index (CEEI) was constructed from multiple remote sensing indicators to quantify ecological quality, and Cohen's d was used to compare differences among morphological types. Applied to the Hangzhou Bay Area (1990–2020), the framework demonstrated that: (i) 25.48 % of cropland underwent morphological transitions, with over 91 % shifting from core to scattered configurations; (ii) Two dominant stepwise pathways—core→edge→scattered (51 %) and core→perforated→edge (25 %)—together with one direct pathway (core→scattered, 12 %) characterized these transitions; (iii) Regional ecological quality continuously declined, with the mean CEEI decreasing from 0.648 to 0.600, accompanied by intensified heat stress (LST relative change: −0.037); (iv) Early-stage transitions from core cropland (core→edge and core→perforated) contributed most to ecological degradation, corresponding to mean CEEI reductions of 0.055 and 0.041, respectively. These findings indicate that preventing the loss of core cropland, especially in the plains of Shanghai and Jiaxing, is key to preserving agricultural ecosystem health. Our proposed framework is flexible in the selection of remote sensing indicators and is broadly applicable to other ecosystems, providing actionable insights for ecological restoration based on morphological configuration.