A comprehensive review of remote sensing in wetland classification and mapping
Shuai Yuan, Xiangan Liang, Tianwu Lin, Shuang Chen, Rui Liu, Jie Wang, Hongsheng Zhang, Peng Gong
TL;DR
Wetlands are critical yet rapidly declining ecosystems, and robust, scalable mapping is essential for conservation and climate-action planning. This review synthesizes a vast body of remote-sensing literature through a meta-analysis of over 1,200 papers, and provides a thorough examination of wetland features, data (optical, SAR, and auxiliaries), classification methods (traditional, ML, and deep learning), mapping inventories, and driving factors. It highlights current research paradigms, critical gaps between data and methods, and limitations of existing products, while outlining future directions such as multi-source data fusion, knowledge-guided explainable AI, foundation models, and task-oriented adaptive mapping to enable dynamic, global wetland monitoring. The work articulates concrete paths for advancing wetland science and management, including dynamic inundation mapping, standardized global classifications, and international collaboration for large-scale, high-frequency products with practical applications in methane estimation, biodiversity conservation, water quality, and flood control.
Abstract
Wetlands constitute critical ecosystems that support both biodiversity and human well-being; however, they have experienced a significant decline since the 20th century. Back in the 1970s, researchers began to employ remote sensing technologies for wetland classification and mapping to elucidate the extent and variations of wetlands. Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change. In this review, we aim to provide a comprehensive perspective and new insights into wetland classification and mapping for readers to answer these questions. First, we conduct a meta-analysis of over 1,200 papers, encompassing wetland types, methods, sensor types, and study sites, examining prevailing trends in wetland classification and mapping. Next, we review and synthesize the wetland features and existing data and methods in wetland classification and mapping. We also summarize typical wetland mapping products and explore the intrinsic driving factors of wetland changes across multiple spatial and temporal scales. Finally, we discuss current limitations and propose future directions in response to global environmental change and technological innovation. This review consolidates our understanding of wetland remote sensing and offers scientific recommendations that foster transformative progress in wetland science.