Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis. The free-to-use GEE platform provides access to (1) petabytes of publicly available remote sensing imagery and other ready-to-use products with an explorer web app; (2) high-speed parallel processing and machine learning algorithms using Google’s computational infrastructure; and (3) a library of Application Programming Interfaces (APIs) with development environments that support popular coding languages, such as JavaScript and Python. Together these core features enable users to discover, analyze and visualize geospatial big data in powerful ways without needing access to supercomputers or specialized coding expertise. The development of GEE has created much enthusiasm and engagement in the remote sensing and geospatial data science fields. Yet after a decade since GEE was launched, its impact on remote sensing and geospatial science has not been carefully explored. Thus, a systematic review of GEE that can provide readers with the “big picture” of the current status and general trends in GEE is needed. To this end, the decision was taken to perform a meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods. A total of 349 peer-reviewed articles published in 146 different journals between 2010 and October 2019 were reviewed. Publications and geographical distribution trends showed a broad spectrum of applications in environmental analyses at both regional and global scales. Remote sensing datasets were used in 90% of studies while 10% of the articles utilized ready-to-use products for analyses. Optical satellite imagery with medium spatial resolution, particularly Landsat data with an archive exceeding 40 years, has been used extensively. Linear regression and random forest were the most frequently used algorithms for satellite imagery processing. Among ready-to-use products, the normalized difference vegetation index (NDVI) was used in 27% of studies for vegetation, crop, land cover mapping and drought monitoring. The results of this study confirm that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.

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

Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics

A global record of annual urban dynamics (1992-2013) from nighttime lights

Developing low carbon economies: An aggregated composite index based on carbon emissions

Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in China and the role of urbanization in aggravating the health burden.

Updating urban extents with nighttime light imagery by using an object-based thresholding method

Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries

Remotely sensed nighttime light (NTL) from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a spatially consistent and cost-effective mean to estimate energy consumption pattern. While previous researches have documented the application of NTL to predict electric power consumption (EPC) with varying degrees of success, few have systematically studied the possible factors affecting the EPC-NTL relationship. Moreover, no substantial research effort has been made to relate overall energy consumption (OEC) to NTL. This study investigated key factors governing the EPC/OEC-NTL relationship by examining the influences of affluence, urbanization, technology, temperature, and NTL pattern. Results show that EPC increased with higher per capital GDP, urbanization rate, and high-technology exports, and lower agricultural development, both globally and regionally. Meanwhile, EPC generally reduced with higher temperature and more agglomerate human activities. A strong OEC-N...

World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery

The Defense Meteorological Satellite Program (DMSP)’s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale.

Yanhua Xie

Papers

Updating urban extents with nighttime light imagery by using an object-based thresholding method

Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries

World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

Monitoring cropland abandonment with Landsat time series