scholarly journals Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

2020 ◽  
Vol 12 (15) ◽  
pp. 2469 ◽  
Author(s):  
Kel N. Markert ◽  
Amanda M. Markert ◽  
Timothy Mayer ◽  
Claire Nauman ◽  
Arjen Haag ◽  
...  
Keyword(s):  
Surface Water ◽  
Historical Analysis ◽  
Google Earth ◽  
Terrain Correction ◽  
Mapping Algorithms ◽  
Significant Difference ◽  
Corrected Data ◽  
Google Earth Engine ◽  
Water Mapping ◽  
Processing Steps

Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

scholarly journals DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 5-10 ◽  
Author(s):  
L. Bi ◽  
B. L. Fu ◽  
P. Q. Lou ◽  
T. Y. Tang
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Google Earth ◽  
Pearl River ◽  
Surface Model ◽  
Landsat 8 ◽  
Pearl River Basin ◽  
Water Index ◽  
Significant Difference ◽  
Google Earth Engine

Abstract. Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.

scholarly journals Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine

2021 ◽  
pp. 100005
Author(s):  
Timothy Mayer ◽  
Ate Poortinga ◽  
Biplov Bhandari ◽  
Andrea P. Nicolau ◽  
Kel Markert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Water ◽  
Google Earth ◽  
Learning Approach ◽  
Google Earth Engine ◽  
Water Mapping

scholarly journals 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

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 138
Author(s):  
Zijie Jiang ◽  
Weiguo Jiang ◽  
Ziyan Ling ◽  
Xiaoya Wang ◽  
Kaifeng Peng ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Surface Water ◽  
Essential Element ◽  
Water Area ◽  
Google Earth ◽  
Water Extraction ◽  
Natural Ecosystem ◽  
The Sustainable Development ◽  
Baiyangdian Lake ◽  
Google Earth Engine

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.

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 Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

2021 ◽  
Vol 13 (1) ◽  
pp. 1290-1302
Author(s):  
Ruimeng Wang ◽  
Li Pan ◽  
Wenhui Niu ◽  
Rumeng Li ◽  
Xiaoyang Zhao ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Vegetation Index ◽  
Yellow River ◽  
Google Earth ◽  
Body Type ◽  
Body Area ◽  
Surface Water Body ◽  
Google Earth Engine ◽  
Xiaolangdi Reservoir

Abstract Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.

scholarly journals Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3010 ◽  
Author(s):  
Ruimeng Wang ◽  
Haoming Xia ◽  
Yaochen Qin ◽  
Wenhui Niu ◽  
Li Pan ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Body ◽  
Vegetation Index ◽  
Water Area ◽  
Google Earth ◽  
Body Area ◽  
Hetao Plain ◽  
Google Earth Engine

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

scholarly journals Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2091 ◽  
Author(s):  
Dong-Dong Zhang ◽  
Lei Zhang
Keyword(s):  
Surface Water ◽  
Land Cover ◽  
Central Region ◽  
Yangtze River ◽  
Industrial Development ◽  
Landsat Imagery ◽  
Google Earth ◽  
Google Earth Engine ◽  
Main Factors ◽  
Lower Yangtze

Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km2 to 1500.5 km2, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km2. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km2. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km2) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed.

scholarly journals Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin

2018 ◽  
Vol 10 (10) ◽  
pp. 1635 ◽  
Author(s):  
Chao Wang ◽  
Mingming Jia ◽  
Nengcheng Chen ◽  
Wei Wang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Minimal Surface ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Google Earth ◽  
Water Dynamics ◽  
Maximal Surface ◽  
Google Earth Engine

Dynamics of surface water is of great significance to understand the impacts of global changes and human activities on water resources. Remote sensing provides many advantages in monitoring surface water; however, in large scale, the efficiency of traditional remote sensing methods is extremely low because these methods consume a high amount of manpower, storage, and computing resources. In this paper, we propose a new method for quickly determining what the annual maximal and minimal surface water extent is. The maximal and minimal water extent in the year of 1990, 2000, 2010 and 2017 in the Middle Yangtze River Basin in China were calculated on the Google Earth Engine platform. This approach takes full advantage of the data and computing advantages of the Google Earth Engine’s cloud platform, processed 2343 scenes of Landsat images. Firstly, based on the estimated value of cloud cover for each pixel, the high cloud covered pixels were removed to eliminate the cloud interference and improve the calculation efficiency. Secondly, the annual greenest and wettest images were mosaiced based on vegetation index and surface water index, then the minimum and maximum surface water extents were obtained by the Random Forest Classification. Results showed that (1) the yearly minimal surface water extents were 14,751.23 km2, 14,403.48 km2, 13,601.48 km2, and 15,697.42 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (2) The yearly maximal surface water extents were 18,174.76 km2, 20,671.83 km2, 19,097.73 km2, and 18,235.95 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (3) The accuracies of surface water classification ranged from 86% to 93%. Additionally, the causes of these changes were analyzed. The accuracy evaluation and comparison with other research results show that this method is reliable, novel, and fast in terms of calculating the maximal and minimal surface water extent. In addition, the proposed method can easily be implemented in other regions worldwide.

scholarly journals Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine

2019 ◽  
Vol 11 (15) ◽  
pp. 1824 ◽  
Author(s):  
Haoming Xia ◽  
Jinyu Zhao ◽  
Yaochen Qin ◽  
Jia Yang ◽  
Yaoping Cui ◽  
...  
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Water Surface ◽  
Water Area ◽  
Google Earth ◽  
The Past ◽  
Huai River Basin ◽  
Huai River ◽  
Google Earth Engine ◽  
The Huai River Basin

The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages.

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