Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Tamiminia, Haifa; Salehi, Bahram; Mahdianpari, Masoud; Quackenbush, Lindi J.; Adeli, Sarina; Brisco, Brian

doi:10.1016/j.isprsjprs.2020.04.001

Cited by 814 publications

(439 citation statements)

References 90 publications

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“…At the same time, IoT-based low-cost sensors continuously present agronomic and environmental parameters, which can be assimilated with the drone-platforms. The advancement of big data processing environments (e.g., Microsoft Azure, Google Earth Engine (GEE) and machine learning methods (e.g., Table 6) offer a unique opportunity to process these datasets in real-time for better decision making [178,179].…”

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confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.

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confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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“…GEE also enables access to digital images processing and machine learning algorithms as well as to libraries of Application Programming Interfaces (API) (such as JavaScript, Python, and R) and scripting interfaces, in which users can develop their own codes and process data online. Thus, GEE allows the manipulation, analysis, and visualization of geospatial data without the need to access supercomputers [54].…”

Section: Google Earth Engine (Gee)mentioning

confidence: 99%

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Castillo

Cayo

Almeida

et al. 2020

IJGI

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During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.

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“…Despite its advantages, GEE is restricted by some limitations, which can be classified into three categories according to Tamiminia et al [32]: (1) Computation. In our case, GEE would run into memory issues when processing is performed on a huge number of datasets.…”

Section: B Limitations and Uncertaintiesmentioning

confidence: 99%

“…GEE stores petabyte scales of over 40 years of remotely-sensed, climate-weather, geophysical datasets, and additional ready-to-use products [31]. It also enables users to discover, analyze, and visualize geospatial big data in powerful ways without needing access to supercomputers or specialized coding expertise [32]. A series of survey studies, ranging from regional to global scales, have been carried out based on GEE, including land cover/use classification [26,[33][34][35][36], crop mapping and yield estimation [37][38][39][40][41], forest mapping [42,43], surface water detection [44,45], etc.…”

Section: Introductionmentioning

confidence: 99%

Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine

Lang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land cover information depicting the complex interactions between human activities and surface change is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results, which hinders its applications in regional studies requiring more accurate land cover data. Informing these issues, here we developed a robust framework to improve the mapping results of 10 m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multisource remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), machine learning algorithms, and a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that Random Forest (RF) models achieved better performance than classification and regression trees (CART), minimum distance (MD), and support vector machine (SVM) models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps, but also provide certain guidelines for future land cover mapping from regional to global scales.

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Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Cited by 814 publications

References 90 publications

A Review on Drone-Based Data Solutions for Cereal Crops

A Review on Drone-Based Data Solutions for Cereal Crops

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine

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