
Progress and Prospect of Flood Monitoring with Fengyun Meteorological Satellite
DOI:
https://doi.org/10.30564/jees.v7i12.12274Abstract
With the intensification of global climate change, flood disasters have become increasingly frequent, and satellite remote sensing has become a core technical means for flood monitoring. The Fengyun meteorological satellites, independently developed by China, hold irreplaceable application value in the timely and efficient monitoring of flood disasters. As a systematic review, this study aims to address the lack of systematic regarding the evolutionary trajectory and application status of Fengyun satellites in flood monitoring. By integrating relevant domestic and international research, it systematically reviws the FengYun-1 to FengYun-4 satellite series in flood monitoring and their application practices on a global scale, and clarifies the complete evolutionary of water body identification technologies—from the early visual interpretation method and the threshold method that dominated in the 1980s–1990s, to the machine learning method emerged in the 1990s, and further to the mixed-pixel decomposition technology pursuing sub-pixel-level accuracy. This study identifies the applicable scenarios and limitations of various water body identification technologies, analyzes the key issues in current applications, summarizes the core advantages of Fengyun meteorological satellites and technical bottlenecks that need to be overcome, and provides an outlook on future development directions in flood monitoring. Finally, it offers systematic theoretical references and practical guidance for the technological upgrading and operational application of flood monitoring based on China's independent satellite remote sensing.
Keywords:
Fengyun Meteorological Satellite; Flood Water Body; Remote Sensing Monitoring; Principles and MethodsReferences
[1] Huo, H., Liu, Y., Li, Y., 2024. Hot spot tracking of flood remote sensing research over the past 22 years: Abibliometric analysis using CiteSpace. Journal of Earth Environment. 15(4), 612–623.DOI: https://doi.org/10.7515/JEE221021 (in Chinese)
[2] Feng, L., Pi, X., 2025. Remote sensing monitoring methods, applications, and challenges for surface water bodies. National Remote Sensing Bulletin. 29(6), 2139–2161. DOI: https://doi.org/10.11834/jrs.20254360 (in Chinese)
[3] Guan, M., Zhang, Y., Li, Y., et al., 2025. Current development and future trends of Fengyun meteorological satellites. Advances in Earth Science. 40(2), 138–154. DOI: https://doi.org/10.11867/j.issn.1001-8166.2025.009 (in Chinese)
[4] Xu, J., Nu, Y., Dong, C., et al., 2006. Ground Segments for FY Meteorological Satellites. Strategic Study of CAE. 8(11), 13–18. (in Chinese)
[5] Gao, H., Li, H., 2001. Application and prospect of Fengyun-1C satellite. China Aerospace. (08), 10–13. (in Chinese)
[6] Li, D., Wu, B.S., Chen, B.W., et al., 2020. Review of water body information extraction based on satellite remote sensing. Journal of Tsinghua University (Science and Technology). 60(2), 147–161. https://doi.org/10.16511/j.cnki.qhdxxb.2019.22.038 (in Chinese)
[7] Deutsch, M., Ruggles, F., 1974. Optical data processing and projected applications of the ERTS-1 imagery covering the 1973 Mississippi River Valley floods. Water Resources Bulletin. 10(5), 1023–1039. DOI: https://doi.org/10.1111/j.1752-1688.1974.tb00622.x
[8] Bhavsar, P.D., 1984. Review of remote sensing applications in hydrology and water resources management in India. Advances in Space Research. 4(11), 193–200. DOI: https://doi.org/10.1016/0273-1177(84)90411-3
[9] Tang, Q., Gao, H., Lu, H., et al., 2009. Remote sensing hydrology. Progress in Physical Geography: Earth and Environment. 33(4), 490–509. DOI: https://doi.org/10.1177/0309133309346650
[10] Huang, C., Chen, Y., Zhang, S., et al., 2018. Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics. 56(2), 333–360. DOI: https://doi.org/10.1029/2018RG000598
[11] Su, L., Li, Z., Gao, F., et al., 2021. A review of remote sensing image water extraction. Remote Sensing for Land and Resources. 33(1), 9–11. DOI: https://doi.org/10.6046/gtzyyg.2020170 (in Chinese)
[12] Feng, S., Yang, Q., Jia, W., et al., 2024. Information extraction of inland surface water bodies based on optical remote sensing: A review. Remote Sensing for Natural Resources. 36(3), 41–56. DOI: https://doi.org/10.6046/zrzyyg.2023123 (in Chinese)
[13] Bartolucci, L.A., Robinson, B.F., Silva, L.R.F., 1977. Field measurements of the spectral response of natural waters. Photogrammetric Engineering & Remote Sensing. 43(5). Available online: https://ntrs.nasa.gov/citations/19770050800
[14] Yao, J., Zheng, W., Shao, J., 2018. Application of FY-3/MERSI monitoring water body change method in Huaihe river basin. Journal of China Hydrology. 38(3), 66–68, 96. (in Chinese)
[15] Li, J., Ma, R., Cao, Z., et al., 2022. Satellite detection of surface water extent: A review of methodology. Water. 14(7), 1148. DOI: https://doi.org/10.3390/w14071148
[16] Du, Y., Zhou, C., 1998. Automatically Extracting remote sensing information for water bodies. Journal of Remote Sensing. 2(4), 264–269. (in Chinese)
[17] Du, J., Huang, Y., Feng, X.Z., et al., 2001. Study on water bodies extraction and classification from SPOT image. Journal of Remote Sensing. 5(3), 214–219. (in Chinese)
[18] Chen, C., Chen, H., Liang, J., et al., 2022. Extraction of water body information from remote sensing imagery considering greenness and wetness based on tasseled cap transformation. Remote Sensing. 14(13), 3001. DOI: https://doi.org/10.3390/rs14133001
[19] Huang, S., Nie, Z., Chen, X., et al., 2019. Remote sensing monitoring of the water area and its changing characteristics in Poyang Lake based on MERSI and MODIS Data. Acta Agriculturae Universitatis Jiangxiensis. 41(3), 610–618. DOI: https://doi.org/10.13836/j.jjau.2019071
[20] McFeeters, S.K., 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 17(7), 1425–1432. DOI: https://doi.org/10.1080/01431169608948714
[21] Feyisa, G.L., Meily, H., Fensholt, R., et al., 2014. Automated water extraction index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment. 140, 23–35. DOI: https://doi.org/10.1016/j.rse.2013.08.029
[22] Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. DOI: https://doi.org/10.1080/01431160600589179
[23] Ding, F., 2009. A new method for fast information extraction of water bodies using remotely sensed data. Remote Sensing Technology and Application, 24(2), 167–171. DOI: https://doi.org/10.11873/j.issn.1004-0323.2009.2.167 (in Chinese)
[24] Cao, R.L., Li, C.J., Liu, L.Y., et al., 2008. Extracting miyun reservoir’s water area and monitoring its change based on a revised normalized different water index. Science of Surveying and Map-ping. 33(2), 158–160. (in Chinese)
[25] Yan P., Zhang Y.J., Zhang Y. 2007. A study on information extraction of water system in semi-arid regions with the enhanced water index (EWI) and GIS based noise remove techniques. Remote Sensing Information. (6), 62–67. (in Chinese)
[26] Ouma, Y.O., Tateishi, R., 2006. A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: An empirical analysis using Landsat TM and ETM+ data. International Journal of Remote Sensing. 27(15), 3153–3181. DOI: https://doi.org/10.1080/01431160500309934
[27] Zhou, Y., Xie, G., Wang, S., et al., 2014. Information Extraction of Thin Rivers around Built-up Lands with False NDWI. Journal of Geo-Information Science. 16(1), 102–107. (in Chinese)
[28] Chen, W.Q., Ding, J.L., Li, Y.H., et al., 2015. Extraction of water information based on China-made GF-1 remote sense image. Resource Science. 37(6), 1166–1172. (in Chinese)
[29] Rad, A.M., Kreitler, J., Sadegh, M., 2021. Augmented normalized difference water index for improved surface water monitoring. Environmental Modelling & Software. 140, 105030. DOI: https://doi.org/10.1016/j.envsoft.2021.105030
[30] Ma, L.Y., Li, J.G., Li, S., 2015. Snowmelt flood disaster monitoring based on FY-3/MERSI in Xinjiang. Remote Sensing for Natural Resources. 27, 73–78. DOI: https://doi.org/10.6046/gtzyyg.2015.04.12
[31] Chen, P., Zhang, Q., Li, Q., 2015. Comparative analysis of several commonly used water extraction methods based on FY-3A/MERSI imagery. Arid Zone Geography. 38(4), 770–778. (in Chinese)
[32] Shao, J., Gao, H., Wang, X., et al., 2020. Application of Fengyun-4 satellite to flood disaster monitoring through a rapid multi-temporal synthesis approach. Journal of Meteorological Research. 34(4), 720–731. DOI: https://doi.org/10.1007/s13351-020-9184-9
[33] Vapnik, V., 2000. The Nature of Statistical Learning Theory. Springer: New York, NY, USA. pp. 1–200. DOI: https://doi.org/10.1007/978-1-4757-3264-1
[34] Paul, A., Tripathi, D., Dutta, D., 2018. Application and comparison of advanced supervised classifiers in extraction of water bodies from remote sensing images. Sustainable Water Resources Management. 4, 905–919. DOI: https://doi.org/10.1007/s40899-017-0184-6
[35] Kloiber, S.M., Brezonik, P.L., Bauer, M.E., 2002. Application of Landsat imagery to regional-scale assessments of lake clarity. Water Research. 36(17), 4330–4340. DOI: https://doi.org/10.1016/S0043-1354(02)00146-X
[36] Li, F., Sang, G.Q., Sun, Y., et al., 2021. Research on methods of complex water body information extraction based on GF-1 satellite remote sensing data. Journal of University of Jinan (Science and Technology). 35(6), 572–579. DOI: https://doi.org/10.13349/j.cnki.jdxbn.20210621.003 (in Chinese)
[37] Costa, V.G., Pedreira, C.E., 2023. Recent advances in decision trees: An updated survey. Artificial Intelligence Review. 56(5), 4765–4800. DOI: https://doi.org/10.1007/s10462-022-10275-5
[38] Rao, P.Z., Jiang, W.G., Wang, X.Y., et al., 2019. Flood disaster analysis based on MODIS data—Taking the flood in dongting lake area in 2017 as an example. Journal of Catastrophology. 34(1), 203–207. (in Chinese)
[39] Du, J., 2017. Research on Lake Wetland Information Extraction and Spatial–Temporal Evolution Characteristics based on Deep Learning [Master’s Thesis]. East China University of Science and Technology: Shanghai, China. (in Chinese)
[40] Liu, C.Z., Shi, J.C., Gao, S., et al., 2010. The study on extracting of water body from MODIS image using an Improved linear mixture model. remote sensing information. (1), 84–88. (in Chinese)
[41] Gong, J., Chang, Z., He, X., et al., 2025. Mountain river waterbody extraction from medium and low-resolution imagery using spectral unmixing and spatial attraction modeling. Journal of Geo-Information Science. 27(8), 1936–1951. (in Chinese)
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Copyright © 2025 Yufeng Lu, Jiali Shao, Yang Zhang, Lei Gao, Wei Zhang, Yi Su

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Yufeng Lu