Crop Classification Using Synthetic Aperture Radar and Optical Imagery

Dear Colleagues,

Accurate and timely data on crop type and crop area are essential for successful agricultural management and environmental sustainability. However, obtaining large-scale maps of crop type and area using satellite imagery can be challenging in certain regions due to factors such as limited ground truth data, inadequate satellite data availability, computational difficulties, and lack of cropland data. Recently, Synthetic Aperture Radar (SAR) data have garnered attention due to their ability to penetrate clouds and operate in all weather conditions. Combining SAR data with optical data can provide valuable insights into crop characteristics over large areas; however, there have been few systematic efforts to integrate both data types. SAR technology is increasingly being used in agricultural monitoring as an active remote sensing system that is not affected by weather conditions or smoke. SAR satellites offer high spatial resolution and frequency, making them a complementary tool in optical imagery for crop monitoring. With the arrival of dense SAR time series from satellites like Sentinel-1 and upcoming missions such as the NASA-ISRO Synthetic Aperture Radar (NISAR), there is growing interest in exploring the potential of SAR for agricultural monitoring. Utilizing SAR technology can provide agronomy managers with a more accurate and reliable understanding of crop types and areas, enabling them to balance agricultural expansion with forest conservation and promote sustainable land management practices.

This Special Issue aims to present the state-of-the-art research in optical, SAR, PolSAR, and PolInSAR imagery for predictive agricultural crop monitoring using publicly available and commercial datasets. We solicit contributions from public and private sectors showcasing the contribution of optical and SAR datasets in agriculture spanning a wide range of topics, including but not limited to the following areas:

• Crop classification using densely sampled time series information of optical and SAR imagery;
• Application of machine learning algorithms in crop classification using SAR and optical imagery;
• Fusion of radar and optical imagery for crop classification;
• Comparison of crop classification using SAR and optical imagery;
• Crop yield prediction using SAR and optical imagery;
• Forest land cover mapping and pattern analysis;
• Biomass estimation from SAR and optical imagery;
• Exploring deep learning's potential for crop classification with SAR and optical imagery.

Dr. Olaniyi Ajadi
Dr. Anu Swatantran
Guest Editors

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• crop classification
• time series analysis
• feature extraction
• machine learning
• deep learning
• image fusion
• synthetic aperture radar (SAR)
• crop health
• land use change detection
• multi-temporal analysis