Abstract
Fengyun-4A (FY-4A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4A images. The maximum between-class variance algorithm (OTSU; developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4A is highly correlated with that from the high-resolution satellite datasets Gaofen-1 (GF-1) and Sentinel-1A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4A satellite data, advantages of the wide coverage, fast acquisition, and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia-Pacific region.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adams, J. B., M. O. Smith, and P. E. Johnson, 1986: Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site. J. Geophys. Res. Solid Earth, 91, 8098–8112, doi: https://doi.org/10.1029/JB091iB08p08098.
Cao, S., H. Li, and W. Ma, 2009: Removing thin cloud arithmetic based on mathematic morphology for remote sensing image. Geogr. Geo-inform. Sci., 25, 30–33. (in Chinese)
Chen, J., L. Ma, X. H. Chen, et al., 2016: Research progress of spectral mixture analysis. J. Remote Sens., 20, 1102–1109, doi: https://doi.org/10.11834/jrs.20166169. (in Chinese)
Ding, L. D., H. Wu, C. J. Wang, et al., 2006: Study of the water body extracting from MODIS images based on spectrum-photometric method. Geom. Spati. Inform. Technol., 29, 25–27, doi: https://doi.org/10.3969/j.issn.1672-5867.2006.06.008. (in Chinese)
Dong, Y. H., 2016: FY-4 meteorological satellite and its application prospect. Aerosp. Shanghai, 33, 1–8, doi: https://doi.org/10.19328/j.cnki.1006-1630.2016.02.001. (in Chinese)
Du, J. K., X. Z. Feng, Z. L. Wang, et al., 2002: The methods of extracting water information from spot image. Chinese Geo-phys. Sci., 12, 68–72, doi: https://doi.org/10.1007/s11769-002-0073-1.
Feng, M., J. O. Sexton, S. Channan, et al., 2016: A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic-spectral classification algorithm. Int. J. Digit. Earth, 9, 113–133, doi: https://doi.org/10.1080/17538947.2015.1026420.
Li, M., D. C. Liew, and L. K. Kwoh, 2003: Producing cloud free and cloud-shadow free mosaic from cloudy IKONOS images. Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Toulouse, France, 3946–3948, doi: https://doi.org/10.1109/IGARSS.2003.1295323.
Li, Z. F., 2015: Application of GF-1 satellite in land-use remote sensing monitoring. Land Resour. Her., 12, 85–88, doi: https://doi.org/10.3969/j.issn.1672-5603.2015.03.016. (in Chinese)
Lu, F., X. H. Zhang, B. Y. Chen, et al., 2017: FY-4 geostationary meteorological satellite imaging characteristics and its application prospects. J. Mar. Meteor., 37, 1–12, doi: https://doi.org/10.19513/j.cnki.issn2096-3599.2017.02.001. (in Chinese)
Lu, J. J., and S. H. Li, 1992: Improvement of the techniques for distinguishing water bodies from TM data. Remote Sens. Environ. China, 7, 17–23. (in Chinese)
Lu, S. L., B. F. Wu, N. Yan, et al., 2011: Water body mapping method with HJ-1A/B satellite imagery. Int. J. Appl. Earth Obs. Geoinform., 13, 428–434, doi: https://doi.org/10.1016/j.jag.2010.09.006.
McFeeters, S. K., 1996: The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens., 17, 1425–1432, doi: https://doi.org/10.1080/01431169608948714.
Milly, P. C. D., R. T. Wetherald, K. A. Dunne, et al., 2002: Increasing risk of great floods in a changing climate. Nature, 415, 514–517, doi: https://doi.org/10.1038/415514a.
Otsu, N., 2007: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9, 62–66, doi: https://doi.org/10.1109/TSMC.1979.4310076.
Potin, P., B. Rosich, N. Miranda, et al., 2016: Sentinel-1 mission status. Procedia Comput. Sci., 100, 1297–1304, doi: https://doi.org/10.1016/j.procs.2016.09.245.
Schubert, A., D. Small, N. Miranda, et al., 2015: Sentinel-1A product geolocation accuracy: Commissioning phase results. Remote Sens., 7, 9431–9449, doi: https://doi.org/10.3390/rs70709431.
Shao, J. L., W. Zheng, and C. Liu, 2015: Analysis of spatial-temporal variations in the main flood season and their influencing factors of the Dongting Lake based on meteorological satellite data. Resour. Environ. Yangtze Basin, 24, 1315–1321, doi: https://doi.org/10.11870/cjlyzyyhj201508008. (in Chinese)
Wang, S. D., M. H. A. Baig, L. F. Zhang, et al., 2015: A simple enhanced water index (EWI) for percent surface water estimation using Landsat data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 90–97, doi: https://doi.org/10.1109/JSTARS.2014.2387196.
Xu, H. Q., 2005: A study on information extraction of water body with the modified normalized difference water index (MND-WI). J. Remote Sens., 9, 589–595. (in Chinese)
Yang, H. B., Z. M. Wang, H. L. Zhao, et al., 2011: Water body extraction methods study based on RS and GIS. Procedia Environ. Sci., 10, 2619–2624, doi: https://doi.org/10.1016/j.proenv.2011.09.407.
Yang, P. G., J. F. Hu, B. H. Yu, et al., 2013: Spatial and temporal characteristics of flood disaster in Asia-Pacific region. J. Shaanxi Norm. Univ. (Nat. Sci. Ed.), 41, 74–81, doi: https://doi.org/10.3969/j.issn.1672-4291.2013.01.017. (in Chinese)
Zhang, P., L. Zhu, S. H. Tang, et al., 2019: General comparison of FY-4A/AGRI with other GEO/LEO instruments and its potential and challenges in non-meteorological applications. Front. Earth Sci., 6, 224, doi: https://doi.org/10.3389/feart.2018.00224.
Zhou, C. H., 1993: Flood disaster surveillance and evaluation by remote sensing. Geogr. Res., 12, 63–68. (in Chinese)
Acknowledgments
The authors thank the Centre for Research on the Epidemiology of Disasters (CRED) and European Space Agency (ESA) Climate Change Initiative (CCI) for providing the datasets. The authors are grateful to the editors and two anonymous reviewers for providing insightful comments that have significantly improved the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Key Research and Development Program of China (2018YFC1506500).
Rights and permissions
About this article
Cite this article
Shao, J., Gao, H., Wang, X. et al. Application of Fengyun-4 Satellite to Flood Disaster Monitoring through a Rapid Multi-Temporal Synthesis Approach. J Meteorol Res 34, 720–731 (2020). https://doi.org/10.1007/s13351-020-9184-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-020-9184-9