Dube, TimothyPandit, SantaOki, Kazuo2026-03-262026-03-262025Pandit, S., Oki, K., Dube, T., Salem, S.I., Okumura, T. and Maki, M., 2025. Land use land cover classification in Japanese wetland and agricultural landscapes via machine learning and multi-source sentinel data. Geocarto International, 40(1), p.2575477.https://doi.org/10.1080/10106049.2025.2575477https://hdl.handle.net/10566/22133This study assessed land use and land cover (LULC) in the Oze wetland and Hatase agricultural fields using Random Forest (RF) and Support Vector Machine (SVM). Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data from 2023–2024 were processed into seasonal median composites. Input features included SAR backscatter coefficients (vertical transmit–vertical receive, vertical transmit–horizontal receive, and their ratio), Sentinel-2 bands (10 m resolution), and vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Bare Soil Index (BSI), and Modified Normalized Difference Water Index (MNDWI). Training and testing data were derived from high-resolution PlanetScope and drone imagery. Models were implemented in Python (Google Colab). Results showed RF consistently outperformed SVM, achieving kappa scores of 81%–83% in Oze and 79%–81% in Hatase, while SVM failed to exceed 80%. RF’s robustness for seasonal LULC mapping highlights its potential to support monitoring and sustainable land management in cloud-prone wetland–agriculture systems.enCross-seasonalDiverse-landscapeDual-sentinelEcosystem classificationMachine learningArticle