Abandoned cropland may lead to a series of issues regarding the environment, ecology, and food security. In hilly areas, cropland is prone to be abandoned due to scattered planting, relatively fewer sunlight hours, and a lower agricultural input–output ratio. Furthermore, the impact of abandoned rainfed cropland differs from abandoned irrigated cropland; thus, the corresponding land strategies vary accordingly. Unfortunately, monitoring abandoned cropland is still an enormous challenge in hilly areas. In this study, a new approach was proposed by (1) improving the availability of Sentinel-1 and Sentinel-2 images by a series of processes, (2) obtaining training samples from multisource data overlay analysis and timeseries viewer tool, (3) mapping annual land cover from all available Sentinel-1 and Sentinel-2 images, training samples, and the random forest classifier, and (4) mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas by assessing land-cover trajectories along with time. The result showed that rainfed cropland had lower F1 scores (0.759 to 0.8) compared to that irrigated cropland (0.836 to 0.879). High overall accuracies of around 0.90 were achieved, with the kappa values ranging from 0.851 to 0.862, which outperformed the existing products in accuracy and spatial detail. Our study provides a reference for extracting the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas.

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.