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

Wang, Chao; Jia, Mingming; Chen, Nengcheng; Wang, Wei

doi:10.3390/rs10101635

Cited by 110 publications

(68 citation statements)

References 56 publications

(35 reference statements)

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“…Google Earth Engine (GEE) is a representative cloud-based platform that can process the data in an intrinsically-parallel way with high-performance and consists of a multi-petabyte remote sensed data, which is preprocessed to ready-to-use and to efficiently access [37]. GEE has been successfully used to map regional, national and global land cover in urban land [38,39], coastal tidal flats [31], mangrove forests [40], crop planting areas [30,41], forests [40] and open surface waters bodies [13,17,18,42].…”

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

et al. 2019

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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 km 2 ). 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. ranks the first among the major river basins in China, and it plays an important role in China's economic and social development. According to the 2016 Huai River Water Resources Bulletin, the surface water resource supply in the Huai River Basin accounts for 74.6% of the total water supply of various water supply projects. Therefore, the temporal and spatial variation characteristics of surface waters need to be accurately mapped to ensure the sustainable economic and social development of the river basin and the stability of the ecosystem.Previous studies mapped the surface water using different data, algorithms and produced different spatial scale production. Satellite-based methods have advantages compared to the traditional methods in surface water mapping due to the low cost, high frequency, and repeatable observations. In recent decades, regional, continental, and global-scales surface water areas have been investigated using the advanced very high resolution radiometer (AVHRR) [5,6], the moderate-resolution imaging spectro-radiometer (MODIS) [7], Landsat [8][9][10][11][12][13][14][15][16][17][18], Sentinel satellite images and so on. Meanwhile, many satellite-based approaches have been developed to detect surface water. The surface water detection algorithms can be roughly divided into general feature classification methods and thematic water surface extraction algorithms [17,19]. General feature cl...

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

et al. 2019

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“…It is necessary to remove the clouds to make use of the acquired images. There is an API called 'simpleCloudScore', provided by the GEE, that calculates the cloud likelihood for every pixel in the range from 1 to 100 by using the normalized difference snow index (NDSI), and the brightness and temperature from the Landsat imagery [26]. The details of simpleCloudScore could be found in the GEE documentation.…”

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confidence: 99%

“…The details of simpleCloudScore could be found in the GEE documentation. The simpleCloudScore is easy to operate and saves time, and some studies used the API to remove the clouds [26,27]. Therefore, we also chose to use it to remove the clouds.…”

Section: Data Preparationmentioning

confidence: 99%

RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations

et al. 2019

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The real-time flood inundation extent plays an important role in flood disaster preparation and reduction. To date, many approaches have been developed for determining the flood extent, such as hydrodynamic models, digital elevation model-based (DEM-based) methods, and remote sensing methods. However, hydrodynamic methods are time consuming when applied to large floodplains, high-resolution DEMs are not always available, and remote sensing imagery cannot be used alone to predict inundation. In this article, a new model for the highly accurate and rapid simulation of floodplains, called “RFim” (real-time inundation model), is proposed to simulate the real-time flooded area. The model combines remote sensing images with in situ data to find the relationship between the inundation extent and water level. The new approach takes advantage of remote sensing images, which have wide spatial coverage and high resolution, and in situ observations, which have continuous temporal coverage and are easily accessible. This approach has been applied in the study area of East Dongting Lake, representing a large floodplain, for inundation simulation at a 30 m resolution. Compared with the submerged extent from observations, the accuracy of the simulation could be more than 90% (the lowest is 93%, and the highest is 96%). Hence, the approach proposed in this study is reliable for predicting the flood extent. Moreover, an inundation simulation for all of 2013 was performed with daily water level observation data. With an increasing number of Earth observation satellites operating in space and high-resolution mappers deployed on satellites, it will be much easier to acquire large quantities of images with very high resolutions. Therefore, the use of RFim to perform inundation simulations with high accuracy and high spatial resolutions in the future is promising because the simulation model is built on remote sensing imagery and gauging station data.

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“…Moreover, how to build and utilize phenological trajectories to discriminate and map mangrove species is still not clear.In recent years, the cloud-based Google Earth Engine platform (GEE, https://earthengine.google. com) has provided great opportunities to individual geoscientists who are interested in geospatial analysis [16,19]. To our benefit, GEE has introduced unlimited possibilities for phenology-based mangrove species mapping in two aspects: (i) the preprocessed high temporal S2 MSI images can be flexibly accessed; and (ii) a wide range of algorithms made to run geospatial analyses can be remotely operated on Google's supercomputers.Thus, the aim of this study was to develop a phenology-based strategy to discriminate among and map the geographical distribution of different mangrove species using dense time series S2 MSI imagery and the GEE platform.…”

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confidence: 99%

“…In recent years, the cloud-based Google Earth Engine platform (GEE, https://earthengine.google. com) has provided great opportunities to individual geoscientists who are interested in geospatial analysis [16,19]. To our benefit, GEE has introduced unlimited possibilities for phenology-based mangrove species mapping in two aspects: (i) the preprocessed high temporal S2 MSI images can be flexibly accessed; and (ii) a wide range of algorithms made to run geospatial analyses can be remotely operated on Google's supercomputers.…”

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confidence: 99%

Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

Jia

Zhang

et al. 2019

Remote Sensing

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Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations. mangrove forests is essential for the accurate formulation of future studies and management of mangrove ecosystems [8][9][10].Remote sensing has served as a sustainable tool in the mapping and monitoring of mangrove forests for decades, primarily because of the logistical and practical difficulties involved in field surveys of the muddy environments [11,12]. Before the launch of the high resolution satellite sensors, it was impossible to accurately discriminate mangrove species with traditional medium resolution satellite data [13]. Recently, with the development of commercial sensors (high spatial resolution, hyperspectral, and active remote sensor), many studies have employed single or combined airborne or satellite imagery to map mangrove species [2,3,8]. However, so far, the mapping of mangrove species with freely accessible imagery still remains a challenge, as mangrove species often exhibit similar spectral signatures and spatial textures [10].The aforementioned issues indicate the need to explore more substantial features to improve the detection of mangrove species from remote sensing data. A phenological trajectory of plants can be acquired from the time series of remote sensing images through delineating the temporal variation in spectrum during the growing period [14]. To date, remotely sensed plant phenology has been widely used to conduct vegetation discrimination, but most of the studied vegetation has been crops or inland forests [15,16]. Although mangroves are evergreen plants, different mangrove species have different phenophase peaks [17]. However, to date, no studies have dealt with mangrove phenological trajectories in remote sensing-based ...

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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

Cited by 110 publications

References 56 publications

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

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

RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations

Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

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