Abstract
Surface Water Mapping (SWM) is essential for studying hydrological and ecological phenomena. SWM holds significant importance in water resource management, environmental conservation, and disaster preparation. Recently, rapid urbanization, overutilization, and environmental degradation have seriously impacted surface water bodies. Rapid advancement in remote sensing data and technologies has promoted the SWM to a new era. Timely and precise SWM is crucial for water resource preservation and planning. This paper critically reviews the extraction of surface water bodies from optical sensors using Spectral Indices (SI), Machine Learning (ML), Deep Learning (DL), and Spectral unmixing with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages, especially over the last decade. The extensive review of SWM reveals that DL outperforms ML and SI. DL outperforms other methods because it incorporates crucial elements in network design, like skip connections, dilation convolution, attention mechanisms, and residual blocks. The spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating the DL technique with spectral unmixing, fusing multisource data (SAR and optical) and integrating it with ancillary data (DEM) is the future direction for improved SWM.
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Data availability
The datasets utilized in this study can be downloaded from the National Remote Sensing Centre (NRSC), Hyderabad, Indian Space Research Organisation (ISRO), India, which is publicly accessible. Since the datasets are publicly accessible, authors are encouraged to access them via the link https:// bhuvan-app3.nrsc.gov.in/data/download/index.php. The images used for research purposes are illustrated in the manuscript.
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Acknowledgements
We gratefully acknowledge the National Remote Sensing Centre (NRSC), Hyderabad, Indian Space Research Organisation (ISRO), India, for supplying the Resourcesat-2: LISS-III image data for educational purposes. We also thank the National Institute of Technology Puducherry in Karaikal, India, for providing research facilities.
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Every author has contributed to the successful compilation of this study. Nagaraj. R: Data collection, validation, Methodology, Formal Analysis, and Writing - original draft. Lakshmi Sutha Kumar: Conceptualization, and Supervision. All authors read and approved the final manuscript.
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Nagaraj, R., Kumar, L.S. Extraction of Surface Water Bodies using Optical Remote Sensing Images: A Review. Earth Sci Inform 17, 893–956 (2024). https://doi.org/10.1007/s12145-023-01196-0
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DOI: https://doi.org/10.1007/s12145-023-01196-0