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Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 83018

Special Issue Editors

Dr. Koreen Millard

E-Mail Website
Guest Editor
Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
Interests: SAR; LiDAR; wetlands; machine learning; random forest
Special Issues, Collections and Topics in MDPI journals
Mr. Alexandre R. Bevington

E-Mail Website
Guest Editor
Research Hydrologist, British Columbia Ministry of Forests, Lands, Natural Resource Operations & Rural Development, 499 George St., Prince George, BC V2M 2H3, Canada
Interests: remote sensing; cryosphere; hydrology; environment; mountains; cold regions; change detection; data science

Special Issue Information

Dear Colleagues,

The ever-increasing global archive of earth observation data enables environmental and societal problems to be assessed and issues to be monitored globally and over long time-scales. Remote sensing software and applications must be able to perform large-area and time-series analysis in a timely manner and at meaningful scales. However, at these spatial and temporal extents, traditional remote sensing workflows that include downloading, processing, and analyzing data locally become challenging for individual scientists and institutions, requiring terabytes to petabytes of storage space and expertise in server or cloud-based storage and processing systems. 

Google Earth Engine (GEE) enables free programmatic access to the MODIS, Landsat 1-5,7, and 8, and Sentinel-1, 2, 3, and 5 archives, with continual updates, as well as many other imagery and ancillary datasets (e.g., land-use data, climate and soil data), through either a Javascript or Python API. As only a browser and internet access is required, these platforms enable access to earth observation data by a new generation of analysts, without the requirement of expensive infrastructure and software. Google provides free training and example codes online to easily enable access to the basic data and algorithms exposed through GEE, and the GEE user community has posted thousands of code and workflow examples online, allowing users to adopt a wide variety of different processing and analysis techniques. Recently, the addition of access to TensorFlow through Google CoLabs has exposed advanced data science and machine learning techniques to users of the GEE earth observation archive. Not only does this enable new tools for the remote sensing scientific community, but it also introduces data scientists to earth observation data analysis using familiar tools and platforms. 

For this Special Issue, we are soliciting contributions that demonstrate new algorithms, methods or applications implemented in either of the GEE APIs. We particularly encourage studies that introduce new analysis techniques, address challenges in implementing large-scale and/or long-time series analysis, and those that share code or application examples. While the main focus of this Special Issue will be on methodological advances using GEE, site-specific case studies that employ GEE functions or tools to advance scientific understanding of environmental and societal issues are also welcome. 

Dr. Koreen Millard
Mr. Alexandre R. Bevington
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Google Earth Engine
  • Environmental change
  • Cloud computing
  • Big data
  • Data democratization
  • Machine learning

Published Papers (15 papers)

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27 pages, 6076 KiB  
Article
Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine
by Sitian Xiong, Priscilla Baltezar, Morgan A. Crowley, Michael Cecil, Stefano C. Crema, Eli Baldwin, Jeffrey A. Cardille and Lyndon Estes
Remote Sens. 2022, 14(19), 4896; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194896 - 30 Sep 2022
Cited by 5 | Viewed by 2007 | Correction
Abstract
Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques [...] Read more.
Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques that produce datasets complementary to national cropland surveys. In this study, we present a flexible Bayesian data synthesis workflow on Google Earth Engine (GEE) that can be used to fuse optical and synthetic aperture radar data and demonstrate its ability to track agricultural change at national scales. We adapted the previously developed Bayesian Updating of Land Cover (Unsupervised) algorithm (BULC-U) by integrating a shapelet and slope thresholding algorithm to identify the locations and dates of cropland expansion and implemented a tiling scheme to allow the processing of large volumes of imagery. We apply this approach to map annual cropland change from 2000 to 2015 for Zambia (750,000 km2), a country that is experiencing rapid growth in agricultural land. We applied our cropland mapping approach to a time series of unsupervised classifications developed from Landsat 5, 7, 8, Sentinel-1, and ALOS PALSAR within 1476 tiles covering Zambia. The annual cropland changes maps reveal active cropland expansion between 2000 to 2015 in Zambia, especially in the Southern, Central, and Eastern provinces. Our accuracy assessment estimates that we have identified 27.5% to 69.6% of the total cropland expansion from 2000 to 2015 in Zambia (commission errors between 6.1% to 37.6%), depending on the slope threshold. Our results demonstrate the usefulness of Bayesian data fusion and shapelet, slope-based thresholding to synthesize optical and synthetic aperture radar for monitoring agricultural changes in situations where training data are scarce. In addition, the annual cropland maps provide one of the first spatially continuous, annually incremented accounts of cropland growth in this region. Our flexible, cloud-based workflow using GEE enables multi-sensor, national-scale agricultural change monitoring at low cost for users. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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25 pages, 36625 KiB  
Article
Mapping Blue and Red Color-Coated Steel Sheet Roof Buildings over China Using Sentinel-2A/B MSIL2A Images
by Alim Samat, Paolo Gamba, Wei Wang, Jieqiong Luo, Erzhu Li, Sicong Liu, Peijun Du and Jilili Abuduwaili
Remote Sens. 2022, 14(1), 230; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010230 - 05 Jan 2022
Cited by 7 | Viewed by 4287
Abstract
Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development [...] Read more.
Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development of Earth observation technologies, remote sensing (RS) provides abundant data to identify and map CCSS materials with different colors in urban areas. However, existing studies are still quite challenging with regards to the data collection and processing costs, particularly in wide geographical areas. Combining free access high-resolution RS data and a cloud computing platform, i.e., Sentinel-2A/B data sets and Google Earth Engine (GEE), this study aims at CCSS material identification and mapping. Specifically, six novel spectral indexes that use Sentinel-2A/B MSIL2A data are proposed for blue and red CCSS material identification, namely the normalized difference blue building index (NDBBI), the normalized difference red building index NDRBI, the enhanced blue building index (EBBI), the enhanced red building index (ERBI), the logical blue building index (LBBI) and the logical red building index (LRBI). These indexes are qualitatively and quantitatively evaluated on a very large number of urban sites all over the P.R. China and compared with the state-of-the-art redness and blueness indexes (RI and BI, respectively). The results demonstrate that the proposed indexes, specifically the LRBI and LBBI, are highly effective in visual evaluation, clearly detecting and discriminating blue and red CCSS covers from other urban materials. Results show that urban areas from the northern parts of P.R. China have larger proportions of blue and red CCSS materials, and areas of blue and red CCSS material buildings are positively correlated with population and urban size at the provincial level across China. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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27 pages, 9208 KiB  
Article
Monitoring the Spatiotemporal Dynamics of Aeolian Desertification Using Google Earth Engine
by Ang Chen, Xiuchun Yang, Bin Xu, Yunxiang Jin, Jian Guo, Xiaoyu Xing, Dong Yang, Ping Wang and Libo Zhu
Remote Sens. 2021, 13(9), 1730; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091730 - 29 Apr 2021
Cited by 11 | Viewed by 2999
Abstract
Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it [...] Read more.
Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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23 pages, 18428 KiB  
Article
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine
by Georg Pointner and Annett Bartsch
Remote Sens. 2021, 13(9), 1626; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091626 - 21 Apr 2021
Cited by 1 | Viewed by 2652
Abstract
Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern [...] Read more.
Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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21 pages, 6961 KiB  
Article
Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Qinghai-Tibetan Plateau from 2001 to 2017
by Yin Zhang, Qingwu Hu and Fengli Zou
Remote Sens. 2021, 13(8), 1566; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081566 - 17 Apr 2021
Cited by 38 | Viewed by 3808
Abstract
The Qinghai-Tibetan Plateau (QTP) is the highest plateau in the world. Under the background of global change, it is of unique significance to study the net primary productivity (NPP) of vegetation on the QTP. Based on the Google Earth Engine (GEE) cloud computing [...] Read more.
The Qinghai-Tibetan Plateau (QTP) is the highest plateau in the world. Under the background of global change, it is of unique significance to study the net primary productivity (NPP) of vegetation on the QTP. Based on the Google Earth Engine (GEE) cloud computing platform, the spatio-temporal variation characteristics of the NPP on the QTP from 2001 to 2017 were studied, and the impacts of climate change, elevation and human activity on the NPP in the QTP were discussed. The mean and trend of NPP over the QTP were “high in the southeast and low in the northwest” during 2001–2017. The trend of NPP was mostly between 0 gC·m−2·yr−1 and 20 gC·m−2·yr−1 (regional proportion: 80.3%), and the coefficient of variation (CV) of NPP was mainly below 0.16 (regional proportion: 89.7%). Therefore, NPP was relatively stable in most regions of the QTP. Among the correlation coefficients between NPP and temperature, precipitation and human activities, the positive correlation accounted for 81.1%, 48.6% and 56.5% of the QTP area, respectively. Among the two climatic factors, the influence of temperature on NPP was greater than that of precipitation. The change of human activities and the high temperature at low altitude had positive effects on the increase of NPP. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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18 pages, 52601 KiB  
Article
Development of Tools for Coastal Management in Google Earth Engine: Uncertainty Bathtub Model and Bruun Rule
by Lucas Terres de Lima, Sandra Fernández-Fernández, João Francisco Gonçalves, Luiz Magalhães Filho and Cristina Bernardes
Remote Sens. 2021, 13(8), 1424; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081424 - 07 Apr 2021
Cited by 9 | Viewed by 4487
Abstract
Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise [...] Read more.
Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE)—a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the sea-level rise impact module of TerrSet—Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses the Bruun rule formula implemented in GEE and can determine the coastline retreat of a profile by creatting a simple vector line from topo-bathymetric data. The model shows a very high correlation (0.97) with a classical Bruun rule study performed in the Aveiro coast (NW Portugal). Therefore, the achieved results disclose that the GEE platform is suitable to perform these analysis. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific community. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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20 pages, 15745 KiB  
Article
Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images
by Ziqi Li, Wenchao Sun, Haiyang Chen, Baolin Xue, Jingshan Yu and Zaifeng Tian
Remote Sens. 2021, 13(6), 1214; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061214 - 23 Mar 2021
Cited by 19 | Viewed by 3102
Abstract
Hydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using a time [...] Read more.
Hydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using a time series of open water surface area data derived from 36 Landsat 8 multispectral images from 2013–2019 and in situ measured water level data. Water area classification was implemented using the Google Earth Engine. Six commonly used indexes for extracting water surface data from satellite images were compared and the best performing index was selected for the water classification. A composite hydrological connectivity index computed from open water area data derived from Landsat 8 images was developed based on several landscape pattern indices and applied to Baiyangdian Lake. The results show that, reflectance in the near-infrared band is the most accurate index for water classification with >98% overall accuracy because of its sensitivity to different land cover types. The slopes of the best-fit linear relationships between the computed hydrological connectivity and observed water level show high variability between years. In most years, hydrological connectivity generally increases when water levels increase, with an average R2 of 0.88. The spatial distribution of emergent plants also varies year to year owing to interannual variations of the climate and hydrological regime. This presents a possible explanation for the variations in the annual relationship between hydrological connectivity and water level. For a given water level, the hydrological connectivity is generally higher in spring than summer and autumn. This can be explained by the fact that the drag force exerted by emergent plants, which reduces water flow, is smaller than that for summer and autumn owing to seasonal variations in the phenological characteristics of emergent plants. Our study reveals that both interannual and seasonal variations in the hydrological connectivity of Baiyangdian Lake are related to the growth of emergent plants, which occupy a large portion of the lake area. Proper vegetation management may therefore improve hydrological connectivity in this wetland. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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19 pages, 7241 KiB  
Article
Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest
by Zhaoming Zhang, Mingyue Wei, Dongchuan Pu, Guojin He, Guizhou Wang and Tengfei Long
Remote Sens. 2021, 13(4), 748; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040748 - 18 Feb 2021
Cited by 14 | Viewed by 3379
Abstract
Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced [...] Read more.
Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. Long time series of satellite data such as Landsat and high-performance computing platforms such as Google Earth Engine (GEE) offer new opportunities to map urban areas. The objective of this research was to determine how annual time series images from Landsat 8 Operational Land Imager (OLI) can effectively be composed to map urban areas in three cities in China in support of GEE. Three reducer functions, ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max() provided by GEE, were selected to construct four schemes to synthesize the annual intensive time series Landsat 8 OLI data for three cities in China. Then, urban areas were mapped based on the random forest algorithm and the accuracy was evaluated in detail. The results show that (1) the quality of annual composite images was improved significantly, particularly in reducing the impact of cloud and cloud shadows, and (2) the annual composite images obtained by the combination of multiple reducer functions had better performance than that obtained by a single reducer function. Further, the overall accuracy of urban areas mapping with the combination of multiple reducer functions exceeded 90% in all three cities in China. In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban areas mapping. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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29 pages, 18427 KiB  
Article
Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation
by Salvatore Praticò, Francesco Solano, Salvatore Di Fazio and Giuseppe Modica
Remote Sens. 2021, 13(4), 586; https://doi.org/10.3390/rs13040586 - 07 Feb 2021
Cited by 120 | Viewed by 13235
Abstract
The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need [...] Read more.
The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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30 pages, 6923 KiB  
Article
Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform
by Seyd Teymoor Seydi, Mehdi Akhoondzadeh, Meisam Amani and Sahel Mahdavi
Remote Sens. 2021, 13(2), 220; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020220 - 10 Jan 2021
Cited by 71 | Viewed by 9960
Abstract
Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their [...] Read more.
Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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16 pages, 4994 KiB  
Article
A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation
by Bingxin Bai, Yumin Tan, Gennadii Donchyts, Arjen Haag and Albrecht Weerts
Remote Sens. 2020, 12(23), 3900; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233900 - 28 Nov 2020
Cited by 10 | Viewed by 3324
Abstract
High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. [...] Read more.
High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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18 pages, 5487 KiB  
Article
Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada
by Meisam Amani, Mohammad Kakooei, Armin Moghimi, Arsalan Ghorbanian, Babak Ranjgar, Sahel Mahdavi, Andrew Davidson, Thierry Fisette, Patrick Rollin, Brian Brisco and Ali Mohammadzadeh
Remote Sens. 2020, 12(21), 3561; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213561 - 30 Oct 2020
Cited by 55 | Viewed by 12679
Abstract
The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between [...] Read more.
The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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26 pages, 7749 KiB  
Article
Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform
by Hanyu Ji, Xing Li, Xinchun Wei, Wei Liu, Lianpeng Zhang and Lijuan Wang
Remote Sens. 2020, 12(17), 2832; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172832 - 01 Sep 2020
Cited by 25 | Viewed by 6103
Abstract
Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining [...] Read more.
Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data using the Google Earth Engine (GEE) platform, we proposed an efficient framework with good transferability for mapping rural settlements in the Yangtze River Delta. To avoid the time-consuming selection of a large number of training samples in the whole study area, we employed four random forest models obtained from the training samples in respective training municipal districts in four different regions to classify other municipal districts in their corresponding region. We found that different features play diverse vital roles in the extraction of rural settlements in various regions. Compared to results only using optical data, accuracies obtained by the proposed method were significantly improved. The average user’s accuracy, producer’s accuracy, overall accuracy, and Kappa coefficient increased by 16.75%, 17.75%, 11.50%, and 14.50% in the four training municipal administrative areas, respectively. The overall accuracy and Kappa coefficient were 96% and 0.84, respectively. By contrast, our classification results are superior to other public datasets. The final mapping results provided a detailed spatial distribution of the rural settlements in the Yangtze River Delta and revealed that the total area of rural settlements is approximately 32,121.1 km2, accounting for 17.41% of the total area. The high-density rural settlements are mainly distributed in the Northern Plain and East Coast, while the low-density rural settlements are located in the Central Hills and Southern Mountain. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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5 pages, 218 KiB  
Correction
Correction: Xiong et al. Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine. Remote Sens. 2022, 14, 4896
by Sitian Xiong, Priscilla Baltezar, Morgan A. Crowley, Michael Cecil, Stefano C. Crema, Eli Baldwin, Jeffrey A. Cardille and Lyndon Estes
Remote Sens. 2023, 15(19), 4692; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15194692 - 25 Sep 2023
Viewed by 490
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
19 pages, 917 KiB  
Technical Note
Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences
by Egor Prikaziuk, Peiqi Yang and Christiaan van der Tol
Remote Sens. 2021, 13(6), 1098; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061098 - 13 Mar 2021
Cited by 4 | Viewed by 5943
Abstract
In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1 [...] Read more.
In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata. Full article
(This article belongs to the Special Issue Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis)
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