Full Lifecycle Monitoring on Drought-Converted Catastrophic Flood Using Sentinel-1 SAR: A Case Study of Poyang Lake Region during Summer 2020

Abstract

During summer 2020, the most catastrophic flood in the 21st century attacked the Poyang Lake region, one of the flood-prone areas in China. To explore the occurrence mechanism and evolution patterns of this drought-converted flood better, a full lifecycle model is developed in this article. Employing Sentinel-1 Synthetic Aperture Radar (SAR) images, with the advantages of high spatial–temporal resolution and all-day and all-weather working capacity, a bimodal threshold was applied to efficiently extract flood inundation mapping. Thus, 61 Sentinel-1 SAR images in 2020 were used to establish inundation sequences for full lifecycle monitoring. This flood presented an abrupt transformation from drought, a long duration, and the slow receding of water, and its area exceeded 3000 km2 from July to early October. In addition, inundation models that reflect the lake area and water level relationship were introduced to assist near-real-time monitoring. Through hydrological and meteorological analysis, compared with results of previous years (from 2010 to 2019), this study found that the water level from July to October in 2020 was at least 17% higher than the mean level at the same period in history and water volume had increased about 44.13 billion m3 during the flooding period. Similarly, the average precipitation from June to September was significantly higher than the same period of previous years. It was the abnormal sustained heavy precipitation and sharp rising of the water level that caused this catastrophic flood. In particular, the Standardized Precipitation Index (SPI) increased from −1.02 in April to 1.31 in July, indicating that the flood was abruptly converted from drought. The inundated areas of several land types during different periods of the full lifecycle were calculated for damage assessment. It was found that cropland was the most heavily impaired with a maximum inundated area of 1375.67 km2, while other land types including forest, grassland, wetland, and impervious surface were relatively less damaged. The study results demonstrate that flood full lifecycle monitoring based on SAR data is helpful to explore the patterns of flood evolution, analyze causes, and assess damage. Simultaneously, focusing on drought-converted floods contributes to the understanding of flood patterns, which provides relevant management departments with decision support for disaster prevention and mitigation.

Characterizing the 2020 summer floods in South China and effects on croplands

“… 38 , 39 Previous studies often used high spatial resolution satellite images in monthly or even daily flood mapping at a small-scale. 33 , 34 In addition, daily MODIS data could satisfy the frequent large-scale observation of floods; however, the spatially explicit flood map cannot be generated by its coarse resolution. 40 , 41 In this study, we realized the large-scale flood mapping using all available S1 SAR data with 10m spatial resolution hosted in the GEE platform, which can ignore the weather effects and realize the near real-time flood monitoring by taking full advantage of a huge amount of data ( Figure S3 ).…”

“…Under such circumstances, near real-time monitoring of floods and flood dynamics in the Yangtze River basin is absolutely and urgently needed. 31 However, existing studies focused on the parts of MLYP, 32 , 33 , 34 such as Poyang Lake and Chao Lake Basin, whereas few studies concerned flood monitoring and flood severity assessment of whole MLYP.…”

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

“…The estimates obtained by Dong et al [39] by using representative concentration pathways (RCPs) suggest an increase in flood magnitude and frequency until 2049. There is still a lack of feasible analysis frameworks to address the coming challenges related to risk and uncertainty, despite Poyang Lake basin being a research hotspot for remote sensing flood-and climate-induced hydrological impact [34,[38][39][40].…”

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

“…However, the estimation accuracy of remotely sensed images is generally lower because satellite image data are easily affected by the atmosphere and clouds [ 25 ]. To address this problem, multi-source remote sensing data have gradually been adopted in soil composition estimation studies [ 26 , 27 ]. The combination of near-Earth unmanned aerial vehicle (UAV) multispectral data and satellite remote sensing data is the most widely used approach because UAV images have high resolution and strong anti-interference characteristics, which compensate for the shortcomings of satellite data [ 18 ].…”