Understanding changes in tropical wetlands with remote sensing, machine learning and land surface modleing

作者：Chandana Pantula, Parker, Robert, 1960-, H. Balzter, Cristina Ruiz Villena, Toby R. Marthews, Khunsa Fatima · 年份：2026 · DOI：10.5194/egusphere-egu26-3075 · 研究领域：Flood Risk Assessment and Management、Remote Sensing in Agriculture、Hydrology and Watershed Management Studies

Tropical wetlands are among the Earth’s most critical ecosystems, playing a vital role in regulating global water and carbon cycles, buffering extreme weather events, and supporting biodiversity that sustains millions of people. Despite their importance, these ecosystems are highly vulnerable to climate change, and our understanding of their seasonal extent, their role in climate mitigation, and their response to changing climatic conditions remains limited. This lack of knowledge hinders the development of effective climate adaptation strategies and constrains projections of future carbon emissions. To address these gaps, this study and related ongoing work aim to develop an integrated framework that combines multiple methodologies, data sources, and analytical tools to improve the monitoring of surface water inundation in major floodplain systems.A key component of this framework is understanding the availability, characteristics, and interpretative value of different remote sensing datasets. As a case study, this work focuses on two major wetland systems: the Sudd in South Sudan and the Pantanal in South America. Satellite observation datasets are compiled and categorized into static and dynamic products. The static datasets include GlobCover, GLWDv2 and SWAMP, while the dynamic datasets comprise WAD2M, JRC Global Surface Water (GSW), CYGNSS water mask, and GRACE Total Water Storage. Static datasets are used to assess long-term changes in wetland extent and classification,...