Mapping Rice Production Typology Using Sentinel-1 Time Series Similarity Analysis to Support Climate Resilient Agriculture in the Vietnamese Mekong Delta

Rice production in the Vietnamese Mekong Delta (VMD) faces rising climate and ecological challenges, prompting national policies to reduce cropping intensity and restore floodplain functions. Synthetic Aperture Radar (SAR) is an effective tool for continuous rice production monitoring, as it is less affected by cloud cover. As such, it can capture complete phenological cycles of rice growth and enable differentiation of rice typologies. This research developed an object-based classification framework using SAR backscatter time series data and Dynamic Time Warping (DTW) to map the Triple Rice and Double Rice production typologies. Landscape segmentation was implemented to delineate homogeneous units for object-based analysis. Sentinel-1 backscatter time series were generated for each unit to capture annual temporal dynamics. Using t-SNE and HDBSCAN clustering, the predominant temporal patterns within the VMD were identified. By associating these with field survey labels, reference temporal profiles for Double and Triple Rice were defined. Finally, landscape units were assigned to rice typologies with the most similar temporal profiles, and the similarity was measured using DTW. This framework was implemented for two years (2019 and 2022). Its accuracy exceeded 83% for 2022. A comparison between 2019 and 2022 indicates a clear transition toward lower cropping intensity.