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This study presents a deep learning method for cloud gap-filling in optical image time series to enhance grassland monitoring, particularly focusing on mowing event detection. By integrating Sentinel-1 SAR data with cloud-free Sentinel-2 optical observations, the proposed CNN-RNN architecture generates continuous NDVI time series, significantly outperforming traditional interpolation methods. The results demonstrate improved mowing detection accuracy, achieving an F1-score of up to 84% in a cloud-prone regi

Author

tanishjagetiya

Language

EN