scholarly journals An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1647
Author(s):  
Wei Jiang ◽  
Yuan Ni ◽  
Zhiguo Pang ◽  
Xiaotao Li ◽  
Hongrun Ju ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Extraction Method ◽  
Spectral Characteristics ◽  
Water Bodies ◽  
The Novel ◽  
Water Index ◽  
Novel Method ◽  
Effective Water ◽  
Sentinel 2

Surface water bodies, such as rivers, lakes, and reservoirs, play an irreplaceable role in global ecosystems and climate systems. Sentinel-2 imagery provides new high-resolution satellite remote sensing data. Based on the analysis of the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel-2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed. Four representative water body types, namely, Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, were selected as study areas to conduct a water body extraction performance comparison with the normalized difference water index (NDWI). We found that (1) the contrast value of the SWI was larger than that of the NDWI in terms of various water body types, including purer water, turbid water, salt water, and floating ice, which suggested that the SWI could achieve better enhancement performance for water bodies. An (2) effective water body extraction method was proposed by integrating the SWI and Otsu algorithm, which could accurately extract various water body types with high overall accuracy. The (3) method effectively extracted large water bodies and wide river channels by suppressing shadow noise in urban areas. Our results suggested that the novel method can achieve efficient water body extraction for rapidly and accurately extracting various water bodies from Sentinel-2 data and the novel method has application potential for larger-scale surface water mapping.

scholarly journals Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Yan Zhou ◽  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Tong Xiao ◽  
Zhiqi Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Landsat 8 ◽  
Open Surface ◽  
Body Mapping ◽  
Water Index ◽  
Landsat 7 ◽  
Surface Water Bodies ◽  
Water Indices ◽  
Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

scholarly journals Extraction of Water-body Area from High-resolution Landsat Imagery

2018 ◽  
Vol 8 (6) ◽  
pp. 4111
Author(s):  
B. Chandrababu Naik ◽  
B. Anuradha
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Body ◽  
Water Area ◽  
Water Bodies ◽  
Landsat 8 ◽  
Indexing Methods ◽  
Water Index ◽  
Surface Water Body ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

scholarly journals A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

2022 ◽  
Vol 14 (2) ◽  
pp. 374
Author(s):  
Xueying Zhou ◽  
Zhaoqiang Huang ◽  
Youchuan Wan ◽  
Bin Ni ◽  
Yalong Zhang ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Body ◽  
Quantitative Research ◽  
Spectral Characteristics ◽  
Quality Parameters ◽  
Living Environment ◽  
Water Bodies ◽  
Labor Costs ◽  
Water Index ◽  
Water Classification

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

scholarly journals A high-resolution inland surface water body dataset for the tundra and boreal forests of North America

2021 ◽  
Author(s):  
Yijie Sui ◽  
Min Feng ◽  
Chunling Wang ◽  
Xin Li
Keyword(s):  
Surface Water ◽  
North America ◽  
Water Body ◽  
Boreal Forests ◽  
Water Bodies ◽  
Permafrost Region ◽  
Morphological Attributes ◽  
Complete Representation ◽  
Surface Water Body ◽  
Sentinel 2

Abstract. Inland surface waters are abundant in the tundra and boreal forests in North America, essential to environments and human societies but vulnerable to climate changes. These high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper we present an inland surface water body inventory (SWBI) dataset for the tundra and boreal forests of North America. Nearly 6.7 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2. The dataset provides geometry coverage and morphological attributes for every water body. During this study we developed an automated approach for detecting surface water extent and identifying water bodies in the 10 m resolution Sentinel-2 multispectral satellite data to enhance the capability for delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America, providing a more complete representation of the region than existing regional datasets, e.g., Permafrost Region Pond and Lake (PeRL). Total accuracy of the detected water extent by SWBI dataset was 96.36 % by comparing to interpreted data for locations randomly sampled across the region. Compared to the 30 m or coarser resolution water datasets, e.g., JRC GSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database (GLWD), the SWBI provided an improved ability on delineating water bodies, and reported higher accuracies in the size, number, and perimeter attributes of water body by comparing to PeRL and interpreted regional dataset. This dataset is available on the National Tibetan Plateau/Third Pole Environment Data Center (TPDC, http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271021 (Feng et al., 2020).

scholarly journals A NEW INDEX FOR IDENTIFYING WATER BODY FROM SENTINEL-2 SATELLITE REMOTE SENSING IMAGERY

2020 ◽  
Vol V-3-2020 ◽  
pp. 33-38
Author(s):  
W. Jiang ◽  
Y. Ni ◽  
Z. Pang ◽  
G. He ◽  
J. Fu ◽  
...  
Keyword(s):  
Water Body ◽  
Salt Lake ◽  
Spectral Characteristics ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Infrared Band ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Shortwave Infrared ◽  
Sentinel 2

Abstract. Water body plays an irreplaceable role in the global ecosystem and climate system. Sentinel-2 is a new satellite data with higher spatial and spectral resolution. Through analysing spectral characteristics of Sentinel-2 satellite imagery, the brightness of water body in vegetation red edge band and shortwave infrared band showe sharply different than that of the not water body. Therefore, a new type of water index SWI (Sentinel-2 Water Index) was proposed by combing those two bands. Four representative water types, which included Taihu Lake, the Yangtze River Estuary, the ChaKa Salt Lake and the Chain Lake, were selected as experimental areas. Normalized difference water index (NDWI) and Sentinel-2 Water Index (SWI) with Otsu method were employed to extract water body. The results showed that overall accuracy and Kappa coefficient of SWI were higher than that of NDWI and SWI was efficient index to rapidly and accurately extract water for Sentinel-2 data. Therefore, SWI had application potential for larger scale water mapping in the future.

scholarly journals Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

2019 ◽  
Vol 11 (3) ◽  
pp. 327 ◽  
Author(s):  
Xia Wang ◽  
Feng Ling ◽  
Huaiying Yao ◽  
Yaolin Liu ◽  
Shuna Xu
Keyword(s):  
Surface Water ◽  
Real Time ◽  
Spatial Resolution ◽  
Water Body ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Body Mapping ◽  
Water Index ◽  
Land Surface Water ◽  
Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

scholarly journals Downscaling MODIS Surface Reflectance to Improve Water Body Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xianghong Che ◽  
Min Feng ◽  
Hao Jiang ◽  
Jia Song ◽  
Bei Jia
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Terrestrial Ecosystems ◽  
Water Area ◽  
Water Bodies ◽  
Tibet Plateau ◽  
Surface Reflectance ◽  
Discrimination Ability ◽  
Automated Method ◽  
Landsat 7

Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

scholarly journals Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253209
Author(s):  
Jianfeng Li ◽  
Biao Peng ◽  
Yulu Wei ◽  
Huping Ye
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Water Extraction ◽  
Practical Significance ◽  
Commission Error ◽  
Complex Environment ◽  
Visual Interpretation ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

scholarly journals STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 641-648
Author(s):  
J. Y. Sun ◽  
G. Z. Wang ◽  
G. J. He ◽  
D. C. Pu ◽  
W. Jiang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Rapid Development ◽  
Kappa Coefficient ◽  
Water Bodies ◽  
Integrated Learning ◽  
Visual Interpretation ◽  
Adaboost Algorithm ◽  
Classification Time ◽  
Better Than

Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).

scholarly journals Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

2020 ◽  
Vol 12 (4) ◽  
pp. 716 ◽  
Author(s):  
Yelong Zhao ◽  
Qian Shen ◽  
Qian Wang ◽  
Fan Yang ◽  
Shenglei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Large Scale ◽  
Measured Data ◽  
Water Bodies ◽  
Polluted Water ◽  
Water Colour ◽  
Hue Angle ◽  
Anomalous Water ◽  
Sentinel 2

As polluted water bodies are often small in area and widely distributed, performing artificial field screening is difficult; however, remote-sensing-based screening has the advantages of being rapid, large-scale, and dynamic. Polluted water bodies often show anomalous water colours, such as black, grey, and red. Therefore, the large-scale recognition of suspected polluted water bodies through high-resolution remote-sensing images and water colour can improve the screening efficiency and narrow the screening scope. However, few studies have been conducted on such kinds of water bodies. The hue angle of a water body is a parameter used to describe colour in the International Commission on Illumination (CIE) colour space. Based on the measured data, the water body with a hue angle greater than 230.958° is defined as a water colour anomaly, which is recognised based on the Sentinel-2 image through the threshold set in this study. The results showed that the hue angle of the water body was extracted from the Sentinel-2 image, and the accuracy of the hue angle calculated by the in situ remote-sensing reflectance Rrs (λ) was evaluated, where the root mean square error (RMSE) and mean relative error (MRE) were 4.397° and 1.744%, respectively, proving that this method is feasible. The hue angle was calculated for a water colour anomaly and a general water body in Qiqihar. The water body was regarded as a water colour anomaly when the hue angle was >230.958° and as a general water body when the hue angle was ≤230.958°. High-quality Sentinel-2 images of Qiqihar taken from May 2016 to August 2019 were chosen, and the position of the water body remained unchanged; there was no error or omission, and the hue angle of the water colour anomaly changed obviously, indicating that this method had good stability. Additionally, the method proposed is only suitable for optical deep water, not for optical shallow water. When this method was applied to Xiong’an New Area, the results showed good recognition accuracy, demonstrating good universality of this method. In this study, taking Qiqihar as an example, a surface survey experiment was conducted from October 14 to 15, 2018, and the measured data of six general and four anomalous water sample points were obtained, including water quality terms such as Rrs (λ), transparency, water colour, water temperature, and turbidity.

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