Fine-Scale Satellite Mapping of Smallholder and Fragmented Agricultural Landscapes

Dear Colleagues,

Agricultural land-cover and land-use information is fundamental for food security assessment, crop production monitoring, land management, and climate-change adaptation. Satellite remote sensing has greatly improved the capacity to observe agricultural systems across large areas and through time. However, accurate mapping remains challenging in smallholder, fragmented, mixed, and highly heterogeneous agricultural landscapes, where field parcels are small, crop types are diverse, management practices vary locally, and spectral signals are often mixed within individual pixels.

Recent advances in high-resolution satellite observations, dense time-series analysis, multi-temporal classification, deep learning, and phenology-aware modelling provide new opportunities to characterize complex agricultural landscapes with greater spatial and temporal detail. In particular, fine-scale field boundary delineation, crop-type mapping, multiple-cropping identification, crop-rotation reconstruction, fallow-land detection, and long-term monitoring of agricultural land-use change are becoming increasingly important for understanding agricultural intensification, abandonment, fragmentation, and consolidation.

This Special Issue focuses on satellite-based approaches for improving the mapping and monitoring of fragmented agricultural landscapes. It aims to bring together methodological innovations, applications, and review studies that advance reliable, scalable, and operational agricultural land-cover and land-use information.

The aim of this Special Issue is to promote recent advances in satellite-based remote sensing for fine-scale agricultural land-cover and land-use mapping in fragmented, heterogeneous, and smallholder-dominated landscapes. It seeks to highlight innovative methods for field boundary delineation, crop-type classification, phenology-aware mapping, multiple-cropping detection, crop-rotation reconstruction, fallow-land identification, and long-term monitoring of agricultural land-use change. By focusing on these challenging agricultural systems, the Special Issue aims to support the development of more accurate, scalable, and operational mapping approaches for sustainable land management and agricultural policy support.

This Special Issue welcomes high-quality original research articles and reviews that explore recent advancements in satellite-based remote sensing for agricultural land-cover and land-use mapping, including, but not limited to, the following advancements:

• Fine-Scale Mapping of Fragmented Agricultural Landscapes: Methods for delineating field boundaries and classifying crop types in smallholder, fragmented, mixed, or highly heterogeneous agricultural systems. Relevant approaches may include object-based analysis, semantic or instance segmentation, foundation models, and sub-pixel mapping.
• Phenology-Aware Crop Classification: Development of methods that incorporate crop calendars, phenological trajectories, environmental constraints, or crop-growth knowledge into time-series classification. Particular attention is given to distinguishing crops with similar spectral characteristics but different planting or harvesting periods.
• Mapping Multiple Cropping, Crop Rotations, and Fallow Land: Satellite-based identification of single-, double-, and triple-cropping systems; crop-sequence reconstruction; crop-rotation mapping; fallow-land detection; and characterization of intercropping or mixed-cropping practices.
• Detection of Agricultural Land-Use Change: Long-term monitoring of cropland expansion, abandonment, intensification, fragmentation, consolidation, conversion, and other agricultural land-use transitions using dense satellite time series.

Dr. Lu Xu
Dr. Mengjia Wang
Guest Editors

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• crop classification
• land cover and land use
• agriculture
• spaceborne remote sensing
• multi-source remote sensing
• deep learning
• time-series analysis