Mapping and Monitoring of Agricultural Land Subtypes with Remote Sensing Technologies

Dear Colleagues,

Agricultural land is not a homogeneous entity but a mosaic of diverse land use types, including paddy fields, rainfed croplands, agriculture facilities (e.g., plastic greenhouses, mulch films), and aquaculture ponds. The precise identification and mapping of these subtypes are critical for optimizing agricultural resource management, ensuring food security, and mitigating environmental risks such as soil degradation, water pollution, and plastic waste accumulation. Traditional broad-scale land cover classifications often fail to capture these subtle yet functionally distinct categories, limiting their utility in policy formulation and precision agriculture.

Remote sensing technologies—due to advancements in high-resolution satellite imagery, hyperspectral sensors, UAVs, and AI-driven analytics—provide unprecedented opportunities for us to achieve the fine-grained discrimination of agricultural land subtypes. For instance, distinguishing plastic-mulched fields from bare soil, mapping fragmented aquaculture ponds in coastal zones, or identifying temporary greenhouse structures requires innovative approaches to spectral analysis, temporal feature extraction, and spatial pattern recognition. This Special Issue seeks to address these challenges by spotlighting cutting-edge methodologies for the fine-scale classification and dynamic monitoring of agricultural land subtypes, with a focus on practical applications in sustainable land use planning and environmental governance.

These should cover or fit within the following themes:

• ​Paddy field delineation: the spectral unmixing of mixed water–vegetation signatures in rice-growing regions.
• ​Agricultural facility monitoring: the detection of plastic greenhouses and mulch films using thermal anomalies or polarization features (SAR).
• ​Aquaculture mapping: the identification of ponds and cages in coastal/inland waters via VHR imagery or Sentinel-1/2 time series.
• ​Dryland vs. irrigated cropland discrimination: the fusion of soil moisture data (SMAP, Sentinel-1) with optical indices.
• ​Temporal dynamics: tracking land use transitions (e.g., the conversion of paddy fields to aquaculture) using dense time-series analyses.
• ​Policy applications: high-resolution datasets for verifying agricultural subsidies, plastic film regulations, or wetland conservation.

Dr. Peng Zhang
Dr. Quanlong Feng
Dr. Pengfei Tang
Dr. Won-Ho Nam
Guest Editors

Manuscript Submission Information

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