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Rice crops are important in the global food economy, and new techniques are being\udimplemented for their effective management. These techniques rely mainly on the changes in the\udphenological cycle, which can be investigated by remote sensing systems. High frequency and high\udspatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather\udconditions for detecting temporal phenological changes. This study focuses on a novel approach for\udgrowth stage determination of rice fields from SAR data using a parameter space search algorithm.\udThe method employs an inversion scheme for a morphology-based electromagnetic backscattering\udmodel. Since such a morphology-based model is complicated and computationally expensive,\uda surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation.\udThe approach is designed to provide estimates of crop morphology and corresponding growth stage\udfrom a continuous growth scale. The accuracy of the proposed method is tested with ground\udmeasurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor\udduring a full growth cycle of rice crops. The analysis shows good agreement for both datasets.\udThe results of the proposed method emphasize the effectiveness of X-band PolSAR data for\udmorphology-based growth stage determination of rice crops

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion