Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Abstract

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

Long Time Series Water Extent Analysis for SDG 6.6.1 Based on the GEE Platform: A Case Study of Dongting Lake

“…In this study, we aimed to explore whether the water extraction results were reliable [49]. This study verified the accuracy through the comparison with the actual Landsat image visual interpretation results and MIWDR-based water body extraction results.…”

Study of Fluctuations in Surface Area of Lake Haramaya using NDWI and MNDWI Methods

“…In particular, (Gebrehiwot et al, 2019) have calculated the surface water area of Lake Haramaya from 1985, which verified the accuracy of the results from 1995 to 2005 in this study. Previous studies on surface extraction using Google Earth Engine Platforms (Jiang et al, 2021), perceptron model (Mishra & Prasad, 2015) provided a great details on dynamics of water surface extraction. The usage of multi temporal Landsat imageries helped to a wide extent in analysing the reflectance property of water as stated by (Rokni et al, 2014;Valeyev et al, 2019;Zhang et al, 2017).…”

Going Beyond Global Indicators—Policy Relevant Indicators for SDG 6 Targets in the Context of Austria

“…Capacity challenges for monitoring and reporting are present in many countries, particularly to cover temporal and spatial dynamics. The use of earth observation to monitor, validate and assess accuracy is promoted and its improvement is topic of on-going research [32,154].…”

“…Besides international support and an integrated approach, they propose the utilization of complementary indicators as one of the options but do not go into detail on potential indicators. Several other papers analyse one or two targets, directing, e.g., on WASH (Water, Sanitation and Hygiene) targets [18,[25][26][27], Target 6.4 [28], 6.5 [29][30][31], 6.6 [32], MoI targets [17] or other aspects such as interlinkages [8,13], gender [33] or citizen science [34].…”