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With escalating human activities and the substantial emissions of greenhouse gases, global warming intensifies. This phenomenon has led to increased occurrences of various extreme hydrological events, precipitating significant changes in lakes and rivers across the Qinghai Tibet Plateau. Therefore, accurate information extraction about and delineation of water bodies are crucial for lake monitoring. This paper proposes a methodology based on the Normalized Difference Water Index (NDWI) and Gumbel distribution to determine optimal segmentation thresholds. Focusing on Qinghai Lake, this study utilizes multispectral characteristics from the US Landsat satellite for analysis. Comparative assessments with seven alternative methods are conducted to evaluate accuracy. Employing the proposed approach, information about water bodies in Qinghai Lake is extracted over 38 years, from 1986 to 2023, revealing trends in area variation. Analysis indicates a rising trend in Qinghai Lake's area following a turning point in 2004. To investigate this phenomenon, Pearson correlation analysis of temperature and precipitation over the past 38 years is used and unveils the fact that slight precipitation impacts on area and that there is a positive correlation between temperature and area. In conclusion, this study employs remote sensing data and statistical analysis to comprehensively investigate mechanisms driving changes in Qinghai Lake's water surface area, providing insights into ecological shifts in lake systems against the backdrop of global warming, thereby offering valuable references for understanding and addressing these changes. [ABSTRACT FROM AUTHOR]

[Objective] The Grand Canal is a complex hydraulic project in the eastern China. This paper studies the flooding and its detrimental impacts in the lowland areas of the canal. [Method] The research was conducted in the Pichang-Tanxin Plain, using multi-source remote sensing data and ground surveys to analyze flooding events. We calculated the normalized difference water body index (NDWI) and the normalized difference vegetation index (NDVI) before and after flooding to assess flood severity, impact on crop growth, as well as the efficacy of hydraulic facilities in mitigating flooding effects. [Result] The NDVI difference between 2004 and 2005 for crops in flooded and non-flooded areas was 0.5 and 0.25, respectively, indicating floodings detrimentally affected crop growth and development. NDWI analysis from 2003 to 2017 demonstrated that the Taierzhuang Control Gate project mitigated flooding impacts on nearby farmlands. Flooding also affected crops in non-inundated regions, with lowland crops more adversely impacted than those outside lowlands. The hydraulic facilities significantly reduced economic losses caused by flooding. [Conclusion] Remote sensing is an effective tool for monitoring inundated areas and assessing the consequences of flooding on crops. The construction of hydraulic facilities plays a crucial role in reducing the detrimental effects of flooding in lowland regions of the Grand Canal. [ABSTRACT FROM AUTHOR]

Hong Lake is the largest lake in Hubei Province. With the increase of Hong Lake economic activity, the area, spatial location and shape of Hong Lake have changed greatly in the past. In this paper, we used the images, which is from the visible infrared imaging radiometer (VIIRS). First, we selected the images of Hong Lake waters on December 6, 2016 and December 26, 2015. Then we extracted the water bodies by the single-band method, spectral relationship method, normalized difference water index (NDWI) were used, and the effect-s were compared. Second, the images of Hong Lake waters in summer and winter were selected from 2012 to 2016, respectively. Last, The NDWI was used to extract the water body and compared with the MODIS image extraction effect in the same period. As a result of the vegetation around Hong Lake, the water is extracted by NDWI and normalized difference vegetation index (NDVI). It is found that for the VIIRS image, the NDWI is the best in the water extraction of Hong Lake. The NDVI + NDWI method is beneficial to the extraction of water covered with aquatic plants. VIIRS image extraction is better than MODIS image. In addition, from the study of VIIRS and MODIS to Hong Lake waters in the five years of water extraction and area calculation, 2012- 2016 period, Hong Lake's average area of 348.213 km2 in flood season, in dry season average area of 349.163 km2. The largest area for the 2012 flood season 389.751 km2, the smallest area of 2016 flood season 306.177 km2. Overall, Hong Lake's area changes little. [ABSTRACT FROM AUTHOR]

• Our method has the potential to extract water with high accuracy on a large scale. • Our method realizes the conversion from unlabeled learning to noisy label learning. • The model performance is significantly higher than traditional methods. • Our method provides a new idea for unsupervised learning. Large-scale monitoring of surface water bodies is of great significance to the sustainable development of regional ecosystems. Remote sensing is currently the main method of global earth observation. On the one hand, the traditional water index is simple and efficient, but it relies on a fixed global threshold, which leads to low accuracy for water extraction. On the other hand, deep learning has achieved state-of-the-art results in classification of spectral data, but it consumes a substantial amount of manpower and time to label sufficient high-quality samples. Furthermore, spectral characteristics of water bodies in different areas vary greatly due to changes in atmospheric conditions and viewing geometry. In this paper, we propose a new accurate water extraction framework based on unsupervised deep learning and NDWI of multispectral images. Binarized NDWI images are used to identify potential water bodies, and deep learning training is performed using these pseudo samples and labels. This process realizes the conversion from unlabeled learning to noisy label learning. First, we proposed a simple and fast binarization algorithm to segment as many real water bodies as possible from NDWI images. Then a set of water confidence assessment rules was constructed from the four aspects of the spectrum, shape, agglomeration, and range. The water segments were scored and sorted to make the model start learning from easy samples and gradually advance to complex samples. Finally, the adjusted co-teaching learning strategy is adopted to filter errors introduced by noisy labels through peer networks with different learning capabilities during training. We tested the accuracy of our method using Gaofen Image Dataset (GID), Sentinel-2 and Landsat images of several water bodies in China. Compared with other methods, our method improved the F1 score by 18.1–40.3% and 6.8–22.2% for GID and Sentinel-2 images, respectively. In addition, our method is more stable in long-term water monitoring. The proposed method has the potential to be used for extracting water bodies with high accuracy on a large scale, especially in areas with complex environments and a lack of deep learning samples. And it provides a new idea for unsupervised learning in the current remote sensing field by fully combining remote sensing expertise and spectral information of ground objects. [ABSTRACT FROM AUTHOR]

Abstract: Water body extraction by using remote sensing has been the most important method in the investigation of water resources, flood hazard prediction assessment and water planning with fast and accurate effectiveness. Multiple methods including unsupervised classification, supervised classification, single-band threshold, inter spectrum relation method and water index method (normalized difference water index, modified normalized difference water index, and new water index) are analyzed. Miyun reservoir and Miyun cit zone are selected as study area to find the most effective water extraction method with different usage in different time. [Copyright &y& Elsevier]

Poyang Lake is the largest freshwater lake in China, an internationally important wetland and the largest migratory bird habitat in Asia. There are numerous seasonal lakes in the Poyang Lake region, and these lakes create a complex and diverse wetland landscape and serve as the main activity area for migratory birds. Seasonal lakes have significant effects on hydrological and ecological processes and are highly susceptible to various changes, but research on seasonal lakes is relatively limited. Based on long-term remote sensing images, this study analyzed the spatiotemporal variation characteristics of seasonal lakes and their influencing factors and ecological effects. The conclusions were as follows: the average water area of seasonal lakes showed a unimodal change during the year, reaching a maximum in July and a minimum in January. The interannual water area fluctuated greatly, and the overall water area showed a small downward trend. In terms of spatial variation, the seasonal lakes were connected with the main lake in the wet season and separated from the main lake in the dry season. Among the natural factors, the influences of temperature, evaporation, and rainfall on the water area of the seasonal lakes were successively reduced. In addition, the incoming water from the Yangtze River had an important influence. Among the human factors, the traditional fishing method "enclosing sublakes in autumn" had the greatest impact on the water area in a specific period, followed by the indirect impact of the Three Gorges Project. [ABSTRACT FROM AUTHOR]

• NDWI can reflect the spatio-temporal hydrological dynamics in the large floodplain. • Significant correlations exist among NDWI, water level and goose occurrence. • The suitable habitats for geese can be extracted quickly by using NDWI range. Hydrological condition is one of the most important factors affecting the abundance and distribution of waterbirds in large freshwater floodplains, as it influences their habitat and food availability. The hydrological condition is commonly indicated with site-based hydrological data such as water level. Site-based data have high temporal resolution but are less effective to represent the spatial characteristics, a requirement for ecological/habitat models. In this study, we propose a method to identify suitable habitats for wintering geese based on the Normalized Difference Water Index (NDWI) derived from Sentinel-2 satellite images. The relationships between NDWI, water level and geese occurrence were analyzed. The results showed that the NDWI of Poyang Lake can explain 75% of the changes in water level and at least 42% of the geese occurrence during wintering periods (with the exception of February 2019). The monthly NDWI optimal ranges suitable for wintering geese were identified to be from −0.12 to 0 and suitable NDWI threshold range is from −0.27 to 0.14. Based on this threshold range, we identified the suitable habitat for geese and found that the suitable habitat area first increased and later decreased during the whole wintering periods, following an inverted U-shaped dynamic. We concluded that NDWI can be used to improve distribution models of waterbird species to support their protection and wetland management. [ABSTRACT FROM AUTHOR]