Abstract
High temporal resolution water distribution maps are essential for surface water monitoring because surface water exhibits significant inner-annual variation. Therefore, high-frequency remote sensing data are needed for surface water mapping. Dongting Lake, the second-largest freshwater lake in China, is famous for the seasonal fluctuations of its inundation extents in the middle reaches of the Yangtze River. It is also greatly affected by the Three Gorges Project. In this study, we used Sentinel-1 data to generate surface water maps of Dongting Lake at 10 m resolution. First, we generated the Sentinel-1 time series backscattering coefficient for VH and VV polarizations at 10 m resolution by using a monthly composition method. Second, we generated the thresholds for mapping surface water at 10 m resolution with monthly frequencies using Sentinel-1 data. Then, we derived the monthly surface water distribution product of Dongting Lake in 2016, and finally, we analyzed the inner-annual surface water dynamics. The results showed that: (1) The thresholds were −21.56 and −15.82 dB for the backscattering coefficients for VH and VV, respectively, and the overall accuracy and Kappa coefficients were above 95.50% and 0.90, respectively, for the VH backscattering coefficient, and above 94.50% and 0.88, respectively, for the VV backscattering coefficient. The VV backscattering coefficient achieved lower accuracy due to the effect of the wind causing roughness on the surface of the water. (2) The maximum and minimum areas of surface water were 2040.33 km2 in July, and 738.89 km2 in December. The surface water area of Dongting Lake varied most significantly in April and August. The permanent water acreage in 2016 was 556.35 km2, accounting for 19.65% of the total area of Dongting Lake, and the acreage of seasonal water was 1525.21 km2. This study proposed a method to automatically generate monthly surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner.
Highlights
The global extent of 304 million lakes (4.2 million km2 in area) cover about 3% of the earth’s surface (Downing et al, 2006; Pekel et al, 2016)
Low-resolution data with high temporal resolution, such as AVHRR and MODIS data, has been used to map the inner-annual body of water changes (Feng et al, 2012; Jain et al, 2006; Kang & Hong, 2016; Klein et al, 2017; Sun et al, 2014), but the coarse resolution of these images led to misclassification due to the problem of mixed pixels, while images with higher spatial resolution, such as those from Sentinel-2, Landsat and China’s HJ satellites (Feng et al, 2016; Liao et al, 2014; Verpoorter et al, 2014), always have low temporal frequency and irregular image time series because of clouds
The results (Table 3) showed that both VH and VV backscattering coefficients achieved high mapping accuracies, and the water and non-water types were well-distinguished based on the threshold-based classification method
Summary
The global extent of 304 million lakes (4.2 million km in area) cover about 3% of the earth’s surface (Downing et al, 2006; Pekel et al, 2016). These lakes provide water supply, How to cite this article Xing et al (2018), Monitoring monthly surface water dynamics of Dongting Lake using Sentinel-1 data at 10 m. Proposing a water surface identification method which is not affected by the cloud is of importance for inner-annual water surface dynamics monitoring (Pekel et al, 2016), in regions with high cloud cover
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