Constructing nationally comprehensive annual rice paddy maps for Madagascar from 2017 to 2025

Abstract. Reliable information on rice paddy cultivation is essential for food security planning in Madagascar, where rice accounts for more than half of the national caloric intake. However, the scarcity of ground reference data, high prevalence of small cropping areas, and the heterogeneity of agroecological zones have limited the production of high-resolution annual rice paddy maps across the country. Here, we present the first country-wide, annual rice paddy maps of Madagascar at 10 m resolution from 2017 to 2025. Our framework integrates three components: (1) phenology-based pseudo-label generation from 30 m merged Harmonized Landsat Sentinel-2 (HLS) time series, exploiting the flooding-to-greenup signal characteristic of transplanted rice paddy; (2) 10 m two-stage Random Forest classification on Google Satellite Embedding (GSE) annual features, refined through targeted augmentation with a small set of manually labeled samples from low-confidence regions; and (3) harmonic NDVI fitting to characterize annual cropping intensity. The pseudo-labels formed compact and clearly separated clusters in the GSE feature space across all nine years, and only five GSE dimensions consistently contributed to rice discrimination, indicating that pre-trained embeddings encoded phenologically meaningful information for rice paddy. The two-stage classifier achieved an overall accuracy (OA) of 91.2 %, a precision of 99.0 %, a recall of 83.2 %, and an F1-score of 0.904 on independent validation samples, outperforming the SAR-based benchmark product by a wide margin. Our maps indicated an increase in mapped rice paddy extent from 886,112 ha in 2017 to 1,195,766 ha in 2025 (+34.9 %), with most gains occurring along the margins of existing paddies rather than in new frontiers. This study demonstrates that combining phenology-based pseudo-labels with pre-trained satellite embeddings provides a scalable, near label-free approach for rice paddy mapping in data-scarce regions. The data are publicly available at https://doi.org/10.5281/zenodo.20654510 (Kwon et al., 2026).