Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas

Hu, Shunshi; Qin, Jianxin; Ren, Jinchang; Zhao, Huimin; Ren, Jie; Hong, Haoran

doi:10.3390/rs12020243

Cited by 17 publications

(15 citation statements)

References 38 publications

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“…With the rapid development of aerospace technology, remote sensing technology provides advanced methods for many fields such as resource survey, environmental monitoring, regional analysis, and global macro research [ 1 – 3 ]. Accurate extraction of water information from remote sensing images is of great significance to watershed planning and investigation, monitoring and protection of water resources [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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

Peng

Wei

et al. 2021

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Peng

Wei

et al. 2021

PLoS ONE

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“…More generally, the composition of the area in question will impact upon the choice of polarization for land-water classification. Numerous studies show SARs effectiveness for water inundation and flood mapping [37,[46][47][48][49][50], however, the separation of land and water based on backscatter values alone is rarely perfect. Inundated vegetation can cause complex scattering mechanism and is a frequently reported source of omission errors [51,52].…”

Section: Introductionmentioning

confidence: 99%

An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

Druce

Tong

Lei³

et al. 2021

Remote Sensing

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Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

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“…Sentinel-1 has a 6 to 12 day repeat, depending on the area, and has an open access mechanism starting from 2015. There is mounting evidence that Sentinel-1 can be used to identify water bodies and quantify surface water extents [ 9 , 22 , 25 , 26 ]. For example, Zeng et al [ 19 ] used the threshold value of -16.35 dB and the Sentinel-1 VV-polarization imagery to extract the Poyang Lake water extent without evaluating the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Zeng et al [ 19 ] used the threshold value of -16.35 dB and the Sentinel-1 VV-polarization imagery to extract the Poyang Lake water extent without evaluating the classification accuracy. Hu et al [ 9 ] proposed a heuristic and automated water extraction method to extract the water map from the Sentinel-1 data with an overall accuracy between 94.32% and 96.7%. Vickers, et al [ 27 ] used the unsupervised K-means clustering algorithm along with the Sentinel-1 and RADARSAT-2 imagery to create binary maps of the water area.…”

Section: Introductionmentioning

confidence: 99%

“…There are some existing water map products used to depict the extent of global water, such as the Joint Research Centre of the European Commission (JRC) global surface water map [6], global open permanent water bodies [7] and global raster water mask at 250 m resolution [8]. However, these products cannot quickly characterize variations in the surface water due to a lack of sufficient spatial and/or temporal resolutions [9]. what number of days of accumulated precipitation is most correlated with the surface water for the southwest Poyang Lake.…”

Section: Introductionmentioning

confidence: 99%

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High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations

Tian

Wang

Pei

et al. 2020

Sensors

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Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km2 on 2 March 2017 to 1042.57 km2 on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km2 (i.e., increasing from 474.70 km2 up to 708.12 km2). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales.

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Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas

Cited by 17 publications

References 38 publications

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

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

An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations

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