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

Abstract

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.

Multiscale Analysis of Water Area, Level and Flow and Their Relationships for a Large Lake Connected to Rivers: A Case Study of Dongting Lake, China

“…This implies that the variation rates of water area in Dongting Lake can be unsteady and different from those of water level and water flow at Chenglingji station at different scales. Compared with previous studies with relatively high model fitting R 2 values for level-area correlations (>0.9) [24,26], the accuracy at the seasonal scale is acceptable, while that at the annual and monthly scales is relatively low. This is probably because (1) unlike previous studies that mainly focused on the East Dongting Lake area directly related to the Chenglingji hydrological station, this study is based on the entire Dongting Lake area and includes parts such as South Dongting Lake and West Dongting Lake; (2) this model of water level-area and water flow-correlations for the study period from 2000 to 2021 at annual, seasonal, and monthly scales, the average process of area, level and flow will smooth the data variation and make correlations more rough compared to studies based on daily water area and water level data; (3) the limited number of samples at the annual scale may influence the performance and representativeness of models; (4) the relatively high uncertainty in water area at the monthly scale can conceal the correlations to some extent.…”

“…These characteristics may be ignored by studies based on traditional time series analysis methods at a single temporal scale. Some studies have established a fitting model between satellite-derived water area and hydrological monitoring data to explore the relationships between rivers and lakes and to simulate changes in water area [23][24][25][26]. They could reveal the characteristics of the relationship to some extent, but similar to the analysis of lake water area, they usually analyzed at a single scale and established a unified relationship model between hydrological indicators (mainly water level) and area.…”

“…The seasonal changes in the water boundary of the lake are significant, and the differences between the geometry of the lake and rivers are significant. Dongting Lake plays an irreplaceable role in intercepting water from the upper reaches, diverting and storing floods in the middle reaches, alleviating flood disasters in the lower reaches of the Yangtze River, regulating the regional climate, and maintaining the ecosystem balance [24,26,28]. In recent decades, due to increasingly natural activities, especially human activities, the morphology of Dongting Lake has undergone significant changes.…”

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

“…This comprehensive data collection approach ensures that the study is underpinned by a wealth of information spanning multiple domains, providing a holistic understanding of the Khinjhir Lake wetland and the various factors affecting it [50,51].…”

The Responses of N2O, CO2 Emissions, and Bacterial Communities to Nitrogen Addition in Saline–Alkaline Wetlands of Northeast China

“…There are still some wetlands around the world that are not internationally recognized, and studies have shown that remote sensing technology is able to define wetlands [4]. At the same time, after extracting the area of wetlands, it is possible to estimate GHG emissions, which is of great significance for the prevention of global warming [5]. The Zhalong Wetland is a typical inland alkaline wetland ecosystem in Northern China, with an area of about 2100 km 2 , with vegetation types mainly dominated by Phragmites australis [6].…”