Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine

doi:10.1080/2150704X.2020.1757780

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Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine

Journal Article•DOI•

Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine

Xiucheng Yang¹, Yang Chen², Jingzhe Wang³•Institutions (3)

University of Strasbourg¹, Wuhan University², Ontario Ministry of Natural Resources³

28 May 2020-Remote Sensing Letters (Taylor & Francis)-Vol. 11, Iss: 7, pp 687-696

TL;DR: In this paper, multispectral satellite data are applied extensively to monitor the surface water dynamics and its temporal changes in water resource management, which is essential in water resources management.

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Abstract: Detecting the water surface area and its temporal changes is essential in water resource management. Multispectral satellite data are applied extensively to monitor the surface water dynamics becau...

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Journal Article•DOI•

Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine

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Lamuel Chi Hay Chung¹, Jing Xie¹, Chao Ren¹•Institutions (1)

University of Hong Kong¹

15 Jul 2021-Building and Environment

TL;DR: This study is the first attempt to make a cross-comparison of various remote sensing datasets and ML algorithms for LCZ classification using the GEE platform and shows that using “elite variables” in the RF classifier can significantly improve classification accuracy while also reducing computational burden.

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32 citations

Journal Article•DOI•

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

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Jiang Wei, Yuan Ni, Pang Zhiguo, Xiaotao Li, Hongrun Ju, Guojin He, Juan Lv, Yang Kun, June Fu, Xiangdong Qin - Show less +6 more

11 Jun 2021-Water

TL;DR: Based on the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed as mentioned in this paper.

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Abstract: 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. (2) An 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. (3) The 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.

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23 citations

Cites background from "Combined use of Sentinel-2 and Land..."

...Sentinel-2 produces widely used remote sensor imagery with high spatial resolution and a short return period and this imagery has great application potential in large-scale surface water mapping [39,40]....
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Journal Article•DOI•

Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM

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Xiaodong Li¹, Feng Ling¹, Giles M. Foody², Doreen S. Boyd², Lai Jiang, Yihang Zhang¹, Pu Zhou¹, Yalan Wang¹, Rui Chen¹, Yun Du¹ - Show less +6 more•Institutions (2)

Chinese Academy of Sciences¹, University of Nottingham²

03 Sep 2021-Remote Sensing of Environment

TL;DR: In this article, a spatiotemporal surface water mapping (STSWM) method was proposed to predict Landsat-like, 30m, surface water maps at an 8-day time step by integrating topographic information into the analysis.

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23 citations

Journal Article•DOI•

Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change.

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Jiaguang Li¹, Stephen Tooth², Kun Zhang, Yang Zhao¹•Institutions (2)

China University of Geosciences (Wuhan)¹, Aberystwyth University²

15 Jan 2021-Journal of Environmental Management

TL;DR: Application of the study approaches to other dryland rivers will help generate comparative data on the controls, rates, patterns and timescales of channel-floodplain dynamics under scenarios of climate change and direct human impacts, with potential implications for improved river management.

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16 citations

Journal Article•DOI•

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

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Alireza Taheri Dehkordi, Mohammad Javad Valadan Zoej, Hani Ghasemi, Ebrahim Ghaderpour, Quazi K. Hassan - Show less +1 more

30 Jun 2022-Sustainability

TL;DR: In this paper , a novel automated method was proposed for providing training samples based on an iterative K-means refinement procedure, which used the Function of the Mask (Fmask) initial water map to generate final training samples.

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Abstract: Water resources are vital to the survival of living organisms and contribute substantially to the development of various sectors. Climatic diversity, topographic conditions, and uneven distribution of surface water flows have made reservoirs one of the primary water supply resources in Iran. This study used Landsat 5, 7, and 8 data in Google Earth Engine (GEE) for supervised monitoring of surface water dynamics in the reservoir of eight Iranian dams (Karkheh, Karun-1, Karun-3, Karun-4, Dez, UpperGotvand, Zayanderud, and Golpayegan). A novel automated method was proposed for providing training samples based on an iterative K-means refinement procedure. The proposed method used the Function of the Mask (Fmask) initial water map to generate final training samples. Then, Support Vector Machines (SVM) and Random Forest (RF) models were trained with the generated samples and used for water mapping. Results demonstrated the satisfactory performance of the trained RF model with the samples of the proposed refinement procedure (with overall accuracies of 95.13%) in comparison to the trained RF with direct samples of Fmask initial water map (with overall accuracies of 78.91%), indicating the proposed approach’s success in producing training samples. The performance of three feature sets was also evaluated. Tasseled-Cap (TC) achieved higher overall accuracies than Spectral Indices (SI) and Principal Component Transformation of Image Bands (PCA). However, simultaneous use of all features (TC, SI, and PCA) boosted classification overall accuracy. Moreover, long-term surface water changes showed a downward trend in five study sites. Comparing the latest year’s water surface area (2021) with the maximum long-term extent showed that all study sites experienced a significant reduction (16–62%). Analysis of climate factors’ impacts also revealed that precipitation (0.51 ≤ R2 ≤ 0.79) was more correlated than the temperature (0.22 ≤ R2 ≤ 0.39) with water surface area changes.

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14 citations

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Journal Article•DOI•

Google Earth Engine: Planetary-scale geospatial analysis for everyone

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Noel Gorelick¹, M. Hancher¹, Mike J. Dixon¹, Simon Ilyushchenko¹, David Thau¹, Rebecca Moore¹ - Show less +2 more•Institutions (1)

Google¹

06 Jul 2017-Remote Sensing of Environment

TL;DR: Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection.

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6,262 citations

"Combined use of Sentinel-2 and Land..." refers methods in this paper

...In the study, a GEE-based WSA dynamic monitoring is proposed by the use of MNDWI, Otsu adapting threshold and postprocessing (vegetation masking to eliminate paddy field and minimum connection pixels to suppress sparse noise)....
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...The proposed approach is intuitively conducted in GEE and has subsequently achieved robust results....
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...The Google Earth Engine (GEE) (Gorelick et al. 2017), an open-source platform for time-series analysis and large-scale application, is adopted to design the water index-based approach and release the spatiotemporal products....
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...Figure 2(a, c) shows two dates of surface water extents extracted by using the proposed approach in GEE on 28 April 2017 and 17 July 2017....
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...The GEE-based method can obtain high UA, which indicate that the most parts of the obtained surface water maps are correct....
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Journal Article•DOI•

The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features

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Stuart K. McFeeters¹•Institutions (1)

University of Nebraska–Lincoln¹

01 May 1996-International Journal of Remote Sensing

TL;DR: The Normalized Difference Water Index (NDWI) as mentioned in this paper is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery.

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Abstract: The Normalized Difference Water Index (NDWI) is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery. The NDWI makes use of reflected near-infrared radiation and visible green light to enhance the presence of such features while eliminating the presence of soil and terrestrial vegetation features. It is suggested that the NDWI may also provide researchers with turbidity estimations of water bodies using remotely-sensed digital data.

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4,353 citations

"Combined use of Sentinel-2 and Land..." refers methods in this paper

...…to delineate WSA in multi-spectral images, include various easy-implemented water indices, such as normalized difference water index (NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al. 2014), and advanced techniques, such as…...
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...(NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al....
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Journal Article•DOI•

Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery

[...]

Hanqiu Xu¹•Institutions (1)

Fuzhou University¹

20 Jul 2006-International Journal of Remote Sensing

TL;DR: In this paper, the normalized difference water index (NDWI) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI.

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Abstract: The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built‐up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built‐up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built‐up land areas because of its advantage in reducing and even removing built‐up land noise over the NDWI.

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3,234 citations

"Combined use of Sentinel-2 and Land..." refers methods in this paper

...(NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al....
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...Binary segmentation was then conducted to extract water pixels from MNDWI images....
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...At present, numerous methods, dedicated to delineate WSA in multi-spectral images, include various easy-implemented water indices, such as normalized difference water index (NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al. 2014), and advanced techniques, such as supervised classification (Verpoorter et al. 2014), object-based image analysis (OBIA) (Yang and Chen 2017) and subpixel unmixing (Pardo-Pascual et al. 2012)....
[...]
...In the study, a GEE-based WSA dynamic monitoring is proposed by the use of MNDWI, Otsu adapting threshold and postprocessing (vegetation masking to eliminate paddy field and minimum connection pixels to suppress sparse noise)....
[...]
...MNDWI ¼ ρgreen ρSWIR ρgreen þ ρSWIR (1) where ρgreenand ρSWIR are band 3 and band 11 for Sentinel-2 images, and band 3 and band 6 for Landsat 8 images, respectively....
[...]

Journal Article•DOI•

High-resolution mapping of global surface water and its long-term changes

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Jean-François Pekel, Andrew Cottam, Noel Gorelick¹, Alan Belward•Institutions (1)

Google¹

15 Dec 2016-Nature

TL;DR: Using three million Landsat satellite images, this globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities.

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Abstract: A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

...read moreread less

2,469 citations

Additional excerpts

...More important, Pekel et al. (2016) released global surface water (GSW) database by using Landsat series data, which recorded the entire history of water detection on a month-bymonth basis between March 1984 and December 2018....
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Journal Article•DOI•

Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery.

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Gudina Legese Feyisa¹, Henrik Meilby¹, Rasmus Fensholt¹, Simon Richard Proud¹•Institutions (1)

University of Copenhagen¹

01 Jan 2014-Remote Sensing of Environment

TL;DR: A new Automated Water Extraction Index (AWEI) is introduced improving classification accuracy in areas that include shadow and dark surfaces that other classification methods often fail to classify correctly, using Landsat 5 TM images of several water bodies.

...read moreread less

1,158 citations

"Combined use of Sentinel-2 and Land..." refers methods in this paper

...(NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al. 2014), and advanced techniques, such as supervised classification (Verpoorter et al....
[...]
...At present, numerous methods, dedicated to delineate WSA in multi-spectral images, include various easy-implemented water indices, such as normalized difference water index (NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al. 2014), and advanced techniques, such as supervised classification (Verpoorter et al. 2014), object-based image analysis (OBIA) (Yang and Chen 2017) and subpixel unmixing (Pardo-Pascual et al. 2012)....
[...]
...…such as normalized difference water index (NDWI) (McFeeters 1996), modified NDWI (MNDWI) (Xu 2006) and automated water extraction index (AWEI) (Feyisa et al. 2014), and advanced techniques, such as supervised classification (Verpoorter et al. 2014), object-based image analysis (OBIA) (Yang…...
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