| Citation: | Limei Wang, Guowang Jin, Xin Xiong. Flood Duration Estimation Based on Multisensor, Multitemporal Remote Sensing: The Sardoba Reservoir Flood. Journal of Earth Science, 2023, 34(3): 868-878. doi: 10.1007/s12583-022-1670-9
|
Single-sensor monitoring of flood events at high spatial and temporal resolutions is difficult because of the lack of data owing to instrument defects, cloud contamination, imaging geometry. However, combining multisensor data provides an impressive solution to this problem. In this study, 11 synthetic aperture radar (SAR) images and 13 optical images were collected from the Google Earth Engine (GEE) platform during the Sardoba Reservoir flood event to constitute a time series dataset. Threshold-based and indices-based methods were used for SAR and optical data, respectively, to extract the water extent. The final sequential flood water maps were obtained by fusing the results from multisensor time series imagery. Experiments show that, when compare with the Global Surface Water Dynamic (GSWD) dataset, the overall accuracy and Kappa coefficient of the water body extent extracted by our methods range from 98.8% to 99.1% and 0.839 to 0.900, respectively. The flooded extent and area increased sharply to a maximum between May 1 and May 4, and then experienced a sustained decline over time. The flood lasted for more than a month in the lowland areas in the north, indicating that the northern region is severely affected. Land cover changes could be detected using the temporal spectrum analysis, which indicated that detailed temporal information benefiting from the multisensor data is highly important for time series analyses.
| Amitrano, D., Martino, G. D., Iodice, A., 2018. Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 56(6): 1–10. https://doi.org/10.1109/tgrs.2018.2797536 |
| Ban, Y., Jacob, A., 2013. Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping. IEEE Transactions on Geoscience & Remote Sensing, 51(4): 1998–2006. https://doi.org/10.1109/tgrs.2012.2236560 |
| Cao, Z., Zhang, G., Dai, W., 2017. Fast Optical and Sar Water Image Registration Based on Second Otsu And SIFT. Journal of Computer-Aided Design & Computer Graphics, 29(11): 1963–1970. https://doi.org/10.3969/j.issn.1003-9775.2017.11.001 |
| Chaouch, N., Temimi, M., Hagen, S., 2012. A Synergetic Use of Satellite Imagery from SAR and Optical Sensors to Improve Coastal Flood Mapping in the Gulf of Mexico. Hydrological Processes, 26(11): 1617–1628. https://doi.org/10.1002/hyp.8268 |
| Criss, R. E., 2016. Statistics of Evolving Populations and Their Relevance to Flood Risk. Journal of Earth Science, 27(1): 2–8. https://doi.org/10.1007/s12583-015-0641-9 |
|
D'Addabbo, A., Refice, A., Pasquariello, G., et al., 2016. Following Flood Dynamics by SAR/Optical Data Fusion. 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), Bari, Italy. 1–5. |
| Deo, R. C., Byun, H. R., Kim, G. B., et al., 2018. A Real-Time Hourly Water Index for Flood Risk Monitoring: Pilot Studies in Brisbane, Australia, and Dobong Observatory, South Korea. Environmental Monitoring & Assessment, 190(8): 450. https://doi.org/10.1007/s10661-018-6806-0 |
| Duan, K., Mei, Y. D., Zhang, L. P., 2016. Copula-Based Bivariate Flood Frequency Analysis in a Changing Climate—A Case Study in the Huai River Basin, China. Journal of Earth Science, 27(1): 37–46. https://doi.org/10.1007/s12583-016-0625-4 |
| Dwyer, J. L., Roy, D. P., Sauer, B., et al., 2018. Analysis Ready Data: Enabling Analysis of the Landsat Archive. Remote Sensing, 10(9): 1363. https://doi.org/10.3390/rs10091363 |
| Feyisa, G. L., Meilby, H., Fensholt, R., et al., 2014. Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery. Remote Sens. Environ, 140: 23–35. https://doi.org/10.1016/j.rse.2013.08.029 |
| Kay, S. M., Doyle, S. B., 2003. Rapid Estimation of the Range-Doppler Scattering Function. IEEE Transactions on Signal Processing, 51(1): 255–268. https://doi.org/10.1109/tsp.2002.806579 |
| Li, J., Wang, S., 2015. An Automatic Method For Mapping Inland Surface Water Bodies with Radarsat-2 Imagery. Int. J. Remote Sens., 36: 1367–1384. https://doi.org/10.1080/01431161.2015.1009653 |
| Lu, G., Yin, F., Chen, C., et al., 2017. Water Depth Extraction from Landsat-8 Image in Hongze Lake. Beijing Surveying and Mapping, 4: 13–18. https://doi.org/10.19580/j.cnki.1007-3000.2017.04.004 |
| Olasz, A., Kristóf, D., Thai, B. N., et al., 2017. Processing Big Remote Sensing Data for Fast Flood Detection in a Distributed Computing Environment. ISPRS–International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W2: 137–138. https://doi.org/10.5194/isprs-archives-xlii-4-W2-137-2017 |
| Pandey, P., Ali, S. N., Ray, P., 2021. Glacier-Glacial Lake Interactions and Glacial Lake Development in the Central Himalaya, India (1994–2017). Journal of Earth Science, 32(6): 1563–1574. https://doi.org/10.1007/s12583-020-1056-9 |
| Pasqualotto, N., Delegido, J., Wittenberghe, S. V., 2019. Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors, 19(4): 904. https://doi.org/10.3390/s19040904 |
| Pickens, A. H., Hansen, M. C., Hancher, M., et al., 2020. Mapping and Sampling to Characterize Global Inland Water Dynamics from 1999 to 2018 with Full Landsat Time Series. Remote Sens. Environ., 243: 111792. https://doi.org/10.1016/j.rse.2020.111792 |
| Pu, R., Bell, S., Meyer, C., 2014. Mapping and Assessing Seagrass Bed Changes in Central Florida's West Coast Using Multitemporal Landsat TM Imagery. Estuarine Coastal & Shelf Science, 149: 68–79. https://doi.org/10.1016/j.ecss.2014.07.014 |
|
Refice, A., Addabbo, A. D., Lovergine, F. P., et al., 2018. Monitoring Flood Extent and Area through Multisensor, Multi-Temporal Remote Sensing: The Strymonas (Greece) River Flood. In: Refice, A., D'Addabbo, A., Capolongo, D., et al., ed., Flood Monitoring through Remote Sensing. Springer Remote Sensing/Photogrammetry, Berlin. |
| Refice, A., Capolongo, D., Pasquariello, G., 2014. SAR and Insar for Flood Monitoring: Examples with COSMO-Skymed Data. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 7(7): 2711–2722. https://doi.org/10.1109/jstars.2014.2305165 |
| Sarker, C., Mejias, L., Maire, F., et al., 2019. Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information. Remote Sensing, 11(19): 2331. https://doi.org/10.3390/rs11192331 |
| Shah-Hosseini, R., Homayouni, S., Safari, A., 2015. Environmental Monitoring Based on Automatic Change Detection from Remotely Sensed Data: Kernel-Based Approach. Journal of Applied Remote Sensing, 9(1): 234–242. https://doi.org/10.1117/1.jrs.9.095992 |
| Tamiminia, H., Salehi, B., Mahdianpari, M., et al., 2020. Google Earth Engine for Geo-Big Data Applications: A Meta-Analysis and Systematic Review. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.00 |
| Tholey, N., Clandillon, S., Fraipont, P. D., 2015. The Contribution of Spaceborne SAR and Optical Data in Monitoring Flood Events: Examples in Northern and Southern France. Hydrological Processes, 11(10): 1409–1413. https://doi.org/10.1002/(sici)1099–1085(199708)11:10<1409::aid–hyp531>3.0.co,2–v doi: 10.1002/(sici)1099–1085(199708)11:10<1409::aid–hyp531>3.0.co,2–v |
| Tulbure, M. G., Broich, M., Stehman, S. V., et al., 2016. Surface Water Extent Dynamics from Three Decades of Seasonally Continuous Landsat Time Series at Subcontinental Scale in a Semi-Arid Region. Remote Sens. Environ., 178: 142–157. https://doi.org/10.1016/j.rse.2016.02.034 |
| Verpoorter, C., Kutser, T., Seekell, D. A., et al., 2014. A Global Inventory of Lakes Based on High-Resolution Satellite Imagery. Geophysical Research Letters, 41(18): 6396–6402. https://doi.org/10.1002/2014gl060641 |
| Voigt, S., Giulio-Tonolo, F., Lyons, J., et al., 2016. Global Trends in Satellite-Based Emergency Mapping. Science, 353: 247–252. https://doi.org/10.1126/science.aad8728 |
| Wang, X., Zhou, A. G., Sun, Z. Y., 2016. Spatial and Temporal Dynamics of Lakes in Nam Co Basin, 1991–2011. Journal of Earth Science, 27(1): 130–138. https://doi.org/10.1007/s12583-016-0634-3 |
| Wulder, M. A., White, J. C., Loveland, T. R., et al., 2016. The Global Landsat Archive: Status, Consolidation, And Direction. Remote Sens. Environ, 185: 271–283. https://doi.org/10.1016/j.rse.2015.11.032 |
| Yang, X., Chen, L., 2017. Evaluation of Automated Urban Surface Water Extraction from Sentinel-2A Imagery Using Different Water Indices. Journal of Applied Remote Sensing, 11(2): 026016. https://doi.org/10.1117/1.jrs.11.026016 |
| Yang, X., Chen, Y., Wang, J., 2020. Combined Use of Sentinel-2 and Landsat 8 To Monitor Water Surface Area Dynamics Using Google Earth Engine. Remote Sensing Letters, 11(7): 687–696. https://doi.org/10.1080/2150704x.2020.1757780 |
| Young, N. E., Anderson, R. S., Chignell, S. M., et al., 2017. A Survival Guide to Landsat Preprocessing. Ecology, 98(4): 920–32. https://doi.org/10.1002/ecy.1730 |
| Yu, R., Xu, Y., Liu, Y., et al., 2009. Reversing Water Depth in Shallow Lake of Arid Area Using Multi-Spectral Remote Sensing Information. Advances in Water Science, 20(1): 111–117. https://doi.org/10.3321/j.issn:1001-6791.2009.01.018 |
| Zhang, F. Z., Zhang, B., Li, J. S., et al., 2011. Comparative Analysis of Automatic Water Identification Method Based on Multispectral Remote Sensing. Procedia Environ. Sci., 11: 1482–1487. https://doi.org/10.1016/j.proenv.2011.12.223 |
| Zhou, X., Chang, N. B., Li, S., 2009. Applications of SAR Interferometry in Earth and Environmental Science Research. Sensors, 9(3): 1876–1912. https://doi.org/10.3390/s90301876 |
| Zhu, Z., Woodcock, C. E., 2014. Automated Cloud, Cloud Shadow, And Snow Detection in Multitemporal Landsat Data: An Algorithm Designed Specifically for Monitoring Land Cover Change. Remote Sens. Environ., 152: 217–234. https://doi.org/10.1016/j.rse.2014.06.012 |
| Zou, Z., Dong, J., Menarguez, M. A., et al., 2017. Continued Decrease of Open Surface Water Body Area in Oklahoma During 1984–2015. Sci. Total Environ., 595: 451–460. https://doi.org/10.1016/j.scitotenv.2017.03.259 |
| Zou, Z., Xiao, X., Dong, J., et al., 2018. Divergent Trends of Open-Surface Water Body Area in the Contiguous United States from 1984 to 2016. PNAS, 115: 3810–3815. https://doi.org/10.1073/pnas.1719275115 |
Figures(10) / Tables(3)
Copyright © 2013-2020 Journal of Earth Science 鄂ICP备15021562号-2
Tel: +86-27-67885075 Fax: +86-27-67885075 E-mail: xbb@cug.edu.cn
Address: Editorial Office of Journal, China University of Geosciences, Yujiashan, Wuhan, Hubei 430074, P. R. China
Supported by:
Beijing Renhe Information Technology Co. Ltd
E-mail:
info@rhhz.net
DownLoad:
