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

Li, Jianfeng; Peng, Biao; Wei, Yulu; Ye, Huping

doi:10.1371/journal.pone.0253209

Cited by 25 publications

(13 citation statements)

References 32 publications

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“…However, the models often misclassify soil, rock, clouds, ice, and shadow as water and often rely on cloud-free, optical RS imagery, which is not always available. The authors in [157] used masking, filtering, and segmentation algorithms to identify bodies of water in Sri Lanka in complex, mountainous environments. It is challenging to repeatedly produce up-to-date, accurate surface water maps over large areas.…”

Section: Water Mapping and Water Quality Monitoringmentioning

confidence: 99%

“…The authors designed the network to work with many different satellite platforms as long as they have a set group of input bands. The authors in [157] used masking, filtering, and segmentation algorithms to identify bodies of water in Sri Lanka in complex, mountainous environments. They showed that their model performs well even in the presence of shadow or soil and does so much better than other common index-based methods like NDWI, MNDWI, or multi-spectral water index (MuWI-R).…”

Section: Abbreviationsmentioning

confidence: 99%

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Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

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Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

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Section: Water Mapping and Water Quality Monitoringmentioning

confidence: 99%

Section: Abbreviationsmentioning

confidence: 99%

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

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“…Other research papers have adapted the GEE for water body extraction using a variety of satellite sensors and delivery methods. The most recent papers have leveraged the GEE to process both multi-spectral Sentinel-2 (S-2), Landsat-8 and synthetic aperture radar (SAR) Sentinel-1 (S-1) images using deep learning (DL) frameworks [29][30][31]; similar data sources of satellite sensors, histogram based, machine learning (ML), and clustering (i.e., K-Means) classifications have been implemented [32][33][34] to derive a scalable water map. ML and DL coupled with the GEE has the potential to derive high quality maps of water distribution, despite the implementation of the model being very complex in practice.…”

Section: Introductionmentioning

confidence: 99%

Automated Mapping of Wetland Ecosystems: A Study Using Google Earth Engine and Machine Learning for Lotus Mapping in Central Vietnam

Quang

et al. 2023

Water

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Wetlands are highly productive ecosystems with the capability of carbon sequestration, providing an effective solution for climate change. Recent advancements in remote sensing have improved the accuracy in the mapping of wetland types, but there remain challenges in accurate and automatic wetland mapping, with additional requirements for complex input data for a number of wetland types in natural habitats. Here, we propose a remote sensing approach using the Google Earth Engine (GEE) to automate the extraction of water bodies and mapping of growing lotus, a wetland type with high economic and cultural values in central Vietnam. Sentinel-1 was used for water extraction with the K-Means clustering, whilst Sentinel-2 was combined with the machine learning smile Random Forest (sRF) and smile Gradient Tree Boosting (sGTB) models to map areas with growing lotus. The water map was derived from S-1 images with high confidence (F1 = 0.97 and Kappa coefficient = 0.94). sGTB outperformed the sRF model to deliver a growth map with a high accuracy (overall accuracy = 0.95, Kappa coefficient = 0.92, Precision = 0.93, and F1 = 0.93). The total lotus area was estimated at 145 ha and was distributed in the low land of the study site. Our proposed framework is a simple and reliable mapping technique, has a scalable potential with the GEE, and is capable of extension to other wetland types for large-scale mapping worldwide.

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“…Multispectral images have numerous bands, ranging from visible to infrared light, and their extensive spectral information allows for reliable object identification. As a result, multispectral change detection has found widespread application in the fields of environmental monitoring [1][2][3][4], resource inquiry [5][6][7], urban planning [8][9][10], and natural catastrophe assessment [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Zhang

Liu

et al. 2022

Remote Sensing

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Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers’ attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN.

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

Cited by 25 publications

References 32 publications

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Automated Mapping of Wetland Ecosystems: A Study Using Google Earth Engine and Machine Learning for Lotus Mapping in Central Vietnam

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

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