Enhancing the surface type classification of SWOT satellite Pixel Cloud data over small reservoirs through the application of machine learning methods

Monitoring changes in inland waters is crucial due to their links with ecosystems, human populations, and the economy. Satellite missions have long supported this monitoring, and the launch of the Surface Water and Ocean Topography (SWOT) satellite in 2022 has provided unprecedented observations of inland water bodies. However, early SWOT data, specifically the Pixel Cloud (PIXC) product, show limitations in classifying surface types. This study applies Machine Learning (ML) methods, namely K-nearest Neighbor (KNN), Random Forest (RF), and eXtreme Gradient Boosting (XG Boost), to improve surface classification in PIXC data. We trained classification models using features from SWOT and one feature from Landsat imagery: the water occurrence value. Our method was tested on eight reservoirs in Iran: 15-Khordad, Dez, Karun4, Zayandehrud, Agh Chai, Doosti, Shahid Rajaei, and Garan-across available SWOT overpasses from 2023 to 2024. Reservoir surface areas derived from our ML-based classifications were validated against in situ data and compared with the original PIXC and the LakeSP product. Results show consistent improvements across all ML methods. Compared to the original PIXC classification, correlation increased by 79.2\% (KNN), 78.7\% (RF, XG Boost), while NRMSE improved by 24.6\%-27.8\%. KGE scores improved by over 130\% for all methods. Compared to LakeSP, our method reduced NRMSE by 37.8\% (XG Boost), increased correlation up to 123\%, and improved KGE scores by over 175\%. No single ML method consistently outperformed the others, but all showed substantial improvements, demonstrating the potential of ML to enhance SWOT-based inland water monitoring.