Remote sensing monitoring of cropland abandonment at the parcel level based on time-series fitting of cultivation probability values

Abandoned cropland is a critical component of agricultural land-use change, with notable implications for food production and the ecological sustainability. This study proposes an integrated monitoring framework, termed OBIA–LT, to address the temporal complexity and spatial fragmentation of abandoned cropland. Cultivation probability is constructed as a time series and analyzed through LandTrendr segmentation fitting. The use of land parcels as analysis units suppresses pixel-level noise, enabling precise identification of the timing and dynamics of abandoned cropland. The Jianghuai hilly region was selected as the study area, and multi-temporal remote sensing imagery and field samples were used for analysis. Results indicate that abandoned cropland exhibits pronounced spatial clustering, with high-density concentrations in the central and western hilly areas and a scattered distribution in the southern plains. The timing of abandonment shows seasonal patterns, occurring predominantly in spring and winter. Long-term continuous abandonment is rare, with more than half the parcels abandoned for only a single quarter, demonstrating the sensitivity of the OBIA–LT framework to short-term cultivation gaps at a monthly scale. This study confirms the effectiveness of the method in achieving high accuracy and spatiotemporal consistency and provides a valuable reference for large-scale monitoring of abandoned cropland dynamics. • Proposes an OBIA–LT method integrating cultivation probability time series with LandTrendr. • Monthly-scale monitoring captures key temporal nodes and long-term trends of cropland abandonment.