Crop Classification Using Time-Series Sentinel-1 SAR Data: A Comparison of LSTM, GRU, and TCN with Attention

This study investigates the performance of temporal deep learning models with attention mechanisms for crop classification using Sentinel-1 C-band synthetic aperture radar (C-SAR) data. A time series of 16 scenes, acquired at 12-day intervals from 25 April to 22 October 2024, was used to classify six crop types: beans, beetroot, grassland, maize, potato, and winter wheat. Three temporal models—long short-term memory (LSTM), bidirectional gated recurrent unit (Bi-GRU), and temporal convolutional network (TCN)—were evaluated with and without an attention mechanism. All model configurations achieved accuracies above 83%, demonstrating the potential of Sentinel-1 SAR data for reliable, weather-independent crop classification. The TCN with attention model achieved the highest accuracy of 85.7%, significantly outperforming the baseline. LSTM also showed improved accuracy when combined with attention, whereas Bi-GRU did not benefit from the attention mechanism. These results highlight the effectiveness of combining temporal deep learning models with attention mechanisms to enhance crop classification using Sentinel-1 SAR time-series data. This study further confirms that freely available, regularly acquired Sentinel-1 observations are well-suited for robust crop mapping under diverse environmental conditions.