Abstract:

Significance Cropland abandonment in hilly and mountainous regions is a pronounced manifestation of land‐use marginalization, with profound implications for both regional food security and ecosystem service provision. In southern China, this issue is particularly acute due to a confluence of factors including early and rapid urbanization, rugged topographic relief, complex multi‐cropping systems, and substantial rural‐to‐urban labor migration, which have driven widespread abandonment of steep, fragmented terraces. This trend presents a profound dual dilemma: on one hand, the cessation of cultivation diminishes local grain production capacity, amplifies pressure on existing cropland, and threatens national food supplies. On the other hand, the secondary succession of spontaneous vegetation on these deserted parcels offers significant carbon sequestration potential and contributes to biodiversity recovery. Yet, accurately mapping these spatio-temporal patterns is severely hampered by persistent cloud cover and the landscape's complexity. This leaves decision-makers without the timely, high‐resolution maps needed to track abandonment dynamics, uncover their socioeconomic and environmental drivers, and craft land-use policies that holistically balance agricultural output, carbon storage, and landscape resilience. Progress Drawing from literature published since 2014, this paper systematically reviews remote sensing‐based methods for cropland abandonment, revealing a clear developmental trajectory. Methodologically, the approaches have evolved along two parallel paths. First, the monitoring paradigm has shifted from early "state comparison" methods, such as post-classification comparison of discrete multi-temporal images, to modern "process tracking" approaches. These leverage dense time series, utilizing phenology‐aware algorithms such as LandTrendr and BFAST to identify abrupt or gradual breaks in the vegetation trajectory, thus capturing the dynamics of abandonment and distinguishing it from short-term fallows. Second, the identification algorithm has progressed from traditional machine learning classifiers and Object-Based Image Analysis (OBIA), which depend on hand‐crafted features, towards sophisticated deep learning frameworks capable of automatically learning complex spatio-temporal signatures. Concurrently, data pre-processing techniques have advanced significantly, with harmonic analysis, Savitzky-Golay filtering, and the integration of Synthetic Aperture Radar (SAR) data now routinely applied to reconstruct continuous, high-quality time series. Furthermore, this review provides a critical synthesis of common methodological issues, focusing on the spatio-temporal representativeness bias in ground validation samples and the multiple sources of uncertainty stemming from cloud cover, mixed pixels, and phenological variability. Conclusions and Prospects Despite considerable advances, persistent challenges continue to limit operational monitoring. Looking forward, the field must evolve from descriptive mapping toward a truly predictive and decision‐ready framework. This transformation hinges on five interlinked frontiers. First, it requires forging the seamless integration of diverse data streams: fusing optical imagery, radar backscatter, and terrain models within cloud computing environments to yield uninterrupted, high‐resolution time series that capture both abrupt and gradual land‐use changes. Second, it necessitates the establishment of an extensive, stratified ground‐truth network; by systematically sampling high-risk, transitional, and reference plots and collecting synchronized measurements, researchers can iteratively recalibrate classification models and improve their resilience across the region's landscape mosaic. Third, on the algorithmic frontier, hybrid approaches that embed expert‐defined phenological rules within deep learning architectures offer a promising path to robustly disentangle permanent abandonment from temporary fallows and to quantify a continuous "abandonment intensity". Fourth, the deployment of fully automated and reproducible processing pipelines on cloud platforms like Google Earth Engine will democratize access to near-real‐time monitoring and enhance reproducibility through open-source workflows. Finally, anchoring detection within dynamic simulation frameworks (e.g., agent‐based models) driven by historical trajectories and key drivers will allow for the projection of future abandonment of "hotspots". Layering these projections with multi‐criteria risk assessments will yield spatially explicit risk maps to guide precision interventions—such as targeted recultivation subsidies or ecological restoration efforts—enabling sustainable land stewardship that simultaneously safeguards food security and enhances ecosystem resilience.

Key words: cropland abandonment, remote sensing, sustainable utilization, hilly and mountainous areas, Southern China