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California, known for its diverse agriculture, is also a major producer of rice, especially in its northern regions in Sacramento River Valley. Traditional methods, predominantly reliant on optical-based satellite imagery, encounter limitations due to atmospheric interference and sensor resolution. The ability of Synthetic Aperture Radar (SAR) to penetrate atmospheric distortions and exhibit high sensitivity to vegetation structure presents a distinct advantage over optical-based methods. Utilizing Optical and SAR data fusion, this study advances the enhanced pixel-based phenological feature composite (Eppf) method using SVM classification algorithm, which can track phenological changes and patterns, providing valuable insights for agricultural planning and management. We demonstrate that Radar Vegetation Index (RVI) derived from SAR data, offers an improved alternative for identifying and mapping rice fields with enhanced accuracy. Subsequent research will focus on enhancing the suggested approach and investigating its relevance and adaptability to different types of crops.


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