Cropland Change Detection at High Spatial and Temporal Resolutions Based on Short-Term PlanetScope Image Series Using LSTM-FCN-CVAPS Model

Timely detection of cropland change at fine spatial scales is essential for sustainable land management and food security. Satellite observations with high spatial and temporal resolutions enable effective cropland monitoring, offering scientific support for decision-making. However, traditional cropland change detection methods often rely on long-term image series, limiting their ability to detect rapid changes over heterogeneous cropping systems. To address these challenges, we develop a novel framework, named LSTM-FCN-CVAPS, which integrates the advanced deep learning model long short-term memory–fully convolutional network (LSTM-FCN) with the change vector analysis in posterior probability space (CVAPS) method—a technique designed to reduce errors in postclassification change detection. This framework is applied to 3-m spatial resolution PlanetScope (PS) imagery to monitor cropland changes at high spatiotemporal resolution over Dangyang County, a key agricultural region in Hubei Province, China. The proposed method achieves high classification accuracy (OA = 0.9761) and outperforms conventional classification methods. By combining LSTM-FCN with CVAPS, it yields superior accuracy (OA = 0.9452), effectively capturing temporal dynamics within a 10-month period. Applied in Dangyang, the method reveals a 3.9% net reduction in cropland area from 2022 to 2024. Major transitions include cropland to bare land (14.94 km2), forest/grass (10.03 km2), artificial surfaces (5.62 km2), and water (2.14 km2), with strong seasonal patterns observed in the conversions of cropland-to-bare land and cropland-to-artificial surface. The cropland changes were concentrated in the central plains, with minimal changes in the southwest. The proposed method is effective and well-suited to change detection over fragmented croplands, requires short-term time-series input, and is transferable to other agricultural areas, contributing to more informed land use planning and continuous environmental monitoring.