Progress and Trends in the Application of Google Earth and Google Earth Engine

Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

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Earth system science and remote sensing

Proceedings of the IEEE ◽

10.1109/proc.1985.13242 ◽

1985 ◽

Vol 73 (6) ◽

pp. 1118-1127 ◽

Cited By ~ 22

Author(s):

F.P. Bretherton

Keyword(s):

Remote Sensing ◽

Earth System ◽

Earth System Science ◽

System Science

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Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Remote Sensing ◽

10.3390/rs10122027 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2027 ◽

Cited By ~ 13

Author(s):

Itiya Aneece ◽

Prasad Thenkabail

Keyword(s):

United States ◽

Remote Sensing ◽

Hyperspectral Imaging ◽

The United States ◽

Google Earth ◽

Growth Stages ◽

Agricultural Crops ◽

Spectral Library ◽

Crop Types ◽

Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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Integrating airborne remote sensing and field campaigns for ecology and Earth system science

Methods in Ecology and Evolution ◽

10.1111/2041-210x.13463 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1492-1508

Author(s):

K. Dana Chadwick ◽

Philip G. Brodrick ◽

Kathleen Grant ◽

Tristan Goulden ◽

Amanda Henderson ◽

...

Keyword(s):

Remote Sensing ◽

Earth System ◽

Airborne Remote Sensing ◽

Earth System Science ◽

System Science ◽

Field Campaigns

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Review for "Integrating airborne remote sensing and field campaigns for ecology and Earth system science"

10.1111/2041-210x.13463/v1/review1 ◽

2020 ◽

Keyword(s):

Remote Sensing ◽

Earth System ◽

Airborne Remote Sensing ◽

Earth System Science ◽

System Science ◽

Field Campaigns

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OPEN FORIS AND GOOGLE EARTH ENGINE LINKING EXPERT PARTICIPATION WITH NATURAL RESOURCE MAPPING AND REMOTE SENSING TRAINING IN TANZANIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w8-117-2018 ◽

2018 ◽

Vol XLII-4/W8 ◽

pp. 117-122 ◽

Cited By ~ 1

Author(s):

U. Leinonen ◽

J. Koskinen ◽

H. Makandi ◽

E. Mauya ◽

N. Käyhkö

Keyword(s):

Remote Sensing ◽

Open Source ◽

Open Source Software ◽

Google Earth ◽

Earth Observation ◽

Teaching Staff ◽

Participatory Mapping ◽

Observation Data ◽

Data Repositories ◽

Google Earth Engine

<p><strong>Abstract.</strong> There is an increasing amount of open Earth observation (EO) data available, offering solutions to map, assess and monitor natural resources and to obtain answers to global and local societal challenges. With the help of free and open source software (FOSS) and open access cloud computing resources, the remote sensing community can take the full advantage of these vast geospatial data repositories. To empower developing societies, support should be given to higher education institutions (HEIs) to train professionals in using the open data, software and tools. In this paper, we describe a participatory mapping methodology, which utilizes open source software Open Foris and QGIS, various open Earth observation data catalogues, and computing capacity of the free Google Earth Engine cloud platform. Using this methodology, we arranged a collaborative data collection event, Mapathon, in Tanzania, followed by a training of the related FOSS tools for HEIs’ teaching staff. We collected feedback from the Mapathon participants about their learning experiences and from teachers about the usability of the methodology in remote sensing training in Tanzania. Based on our experiences and the received feedback, using a participatory mapping campaign as a training method can offer effective learning about environmental remote sensing through a real-world example, as well as networking and knowledge sharing possibilities for the participating group.</p>

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Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Remote Sensing ◽

10.3390/rs10101509 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1509 ◽

Cited By ~ 60

Author(s):

Lalit Kumar ◽

Onisimo Mutanga

Keyword(s):

Remote Sensing ◽

Google Earth ◽

Earth Observation ◽

African Continent ◽

Ancillary Data ◽

Usage Patterns ◽

Data Portal ◽

Google Earth Engine ◽

Study Sites ◽

Developed Nations

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.

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