Mapping paddy rice using segmentation techniques and phenological metrics derived from Sentinel-2 time series in Senegal

Highlights: What are the main findings? Integration of image segmentation techniques and phenological metric extraction to map rice fields in the Senegal River Delta (SRD). A segmentation-based approach improved field boundary detection and reduced classification uncertainty compared to pixel-based methods. What are the implications of the main findings? Phenological parameters such as the start, peak, and end of the growing season enabled clear discrimination between rice growth patterns. The combined use of temporal and spatial information enhances crop monitoring and supports sustainable rice management practices. Rice field mapping is essential for effective agricultural and water resource management due to high land pressure. This study aims to map paddy rice by combining segmentation techniques and phenological metrics derived from optical time series. Thus, a crop segmentation-based approach was developed using Sentinel-2 imagery (2018–2019) to assess the paddy rice extent in the Senegal River Delta (SRD). Two super-pixel segmentation algorithms were evaluated to optimize the identification of rice plots by integrating spectral and spatial characteristics from the green, red, and near-infrared (NIR) bands. In this study, the Felzenszwalb outperformed the Quickshift algorithm, achieving a median intersection over union (IoU) of 0.25 compared to 0.20 for the segmentation of rice fields. The analysis of NDVI time series enabled the identification of key stages in the rice phenological cycle. Two machine learning algorithms (i.e., Random Forest and XGBoost) were compared for rice crop detection. Random Forest delivered a better performance (AUC = 0.93, OA = 0.98, F1-score = 0.98) than the XGBoost (AUC = 0.92, OA = 0.98, F1-score = 0.98). Overall, the results indicated that the approach could accurately identify paddy rice fields, and thus improve decision making and support food security management in the region.