Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images

Abstract

High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To date, the methods of extracting and analyzing inundation maps of lake sites have been widely discussed, but the method of extracting surface water maps refined by inundation frequency map and the distinction of inundation frequency map from different datasets have not been fully explored. In this study, we leveraged the Google Earth Engine platform to compare and evaluate the effects of a method combining a histogram-based algorithm with a temporal-filtering algorithm in order to obtain high-quality surface water maps. Both algorithms were conducted on Sentinel-1 images over Poyang Lake and Dongting Lake, the two largest lakes in China, respectively. High spatiotemporal time-series analyses of both lakes were implemented between 2017 and 2021, while the inundation frequency maps extracted from Sentinel-1 data were compared with those extracted from Landsat images. It was found that Sentinel-1 can monitor water inundation with a substantially higher accuracy, although minor differences were found between the two sites, with the overall accuracy for Poyang Lake (95.38–98.69%) being higher than that of Dongting Lake (95.05–97.5%). The minimum and maximum water areas for five years were 1232.96 km2 and 3828.36 km2 in Poyang Lake, and 624.7 km2 and 2189.17 km2 in Dongting Lake. Poyang Lake was frequently inundated with 553.03 km2 of permanent water and 3361.39 km2 of seasonal water while Dongting Lake was less frequently inundated with 320.09 km2 of permanent water and 2224.53 km2 of seasonal water. The inundation frequency maps from different data sources had R2 values higher than 0.8, but there were still significant differences between them. The overall inundation frequency values of the Sentinel-1 inundation frequency maps were lower than those of the Landsat inundation frequency maps due to the severe contamination from cloud cover in Landsat imagery, which should be paid attention in practical application.

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

“…In the past five years, it is obvious that the overall water area of Dongting Lake in 2018, 2019, and 2021 is relatively small. In 2020, the overall water area of Dongting Lake is largest, which coincides with the historical hydrological data and the climate of more rainfall and a longer flood season in 2020 [29]. As observed from Figure 9, the changes in the Dongting Lake water area present obvious seasonal rules where the water area presents an increasing trend in the first half of the year and a decreasing trend in the second half of the year.…”

“…Sentinel-2 is a high-resolution multispectral imaging satellite that carries a multispectral Imager (MSI). Sentinel-2 missions include Sentinel-2A (launched in June 2015) and Sentinel-2B (launched in March 2017), it covers 13 bands with a high spatial resolution of 10 m, and the revisit period of the binary system is 5 days [29,30]. Sentinel-2 images can also be freely downloaded from the European Space Agency's data-sharing website (https://scihub.copernicus.eu/dhus/) (accessed on 6 June 2022).…”

“…Zhang et al selected the VV polarization mode of Sentinel-1 and used the field ratio method for threshold segmentation, after which they then realized the dynamic change monitoring of two glacial lakes in the Qinghai-Tibet Plateau region [28]. Wang et al obtained inundation frequency maps of Poyang Lake and Dongting Lake based on the VV backscattering coefficient of Sentinel-1 by using the Google Earth Engine platform [29]. However, the single-polarization information is susceptible to noise, the threshold is determined incorrectly, and the extracted water area is not accurate [27].…”

GRDL: A New Global Reservoir Area‐Storage‐Depth Data Set Derived Through Deep Learning‐Based Bathymetry Reconstruction

“…In summary, the existing methods for assessing reservoir storage variations can be broadly categorized into two groups, both established on the assumption that surface water area can be reliably measured from space (Feyisa et al., 2014; Fisher et al., 2016; Wang et al., 2022). The first category integrates optical imagery, such as data from MODIS (Gao et al., 2012), Landsat (Y. Li et al., 2023; Yao, Livneh, et al., 2023), and Sentinel‐2 (Yao, Minear, et al., 2023), for tracking water area variations with radar altimetry or in situ data to monitor water level changes.…”

“…In summary, the existing methods for assessing reservoir storage variations can be broadly categorized into two groups, both established on the assumption that surface water area can be reliably measured from space (Feyisa et al, 2014;Fisher et al, 2016;Wang et al, 2022). The first category integrates optical imagery, such as data from MODIS (Gao et al, 2012), Landsat (Y.…”