Global scale historical remote sensing data has been accumulated for more than half a century. The remote sensing big data formed by these continuously emerging massive remote sensing data provides abundant data support for Earth science research. Furthermore, it is a new challenge for the rapid processing, analysis and mining of remote sensing big data. The emergence of Remote Sensing Cloud Computing Platform (RS-CCP) provides unprecedented opportunities for remote sensing big data mining. Meanwhile, it completely changes the traditional remote sensing data processing and analysis mode, making it possible to quickly analyze and apply long-term sequences on a global scale.This study systematically combed the state-of-the-art development of Google Earth Engine (GEE), including the origin, current progress, petabyte scale catalog of public and free-to-use geospatial datasets, computing capability for planetary-scale analysis of Earth science data, Application Programming Interface (API), and GEE Apps. Combined with GEE, the RS-CCPs at home and abroad, including NASA Earth Exchange, Descartes Labs, Amazon Web Services (AWS), Data Cube, Copernicus Data and Exploitation Platform-DE (CODE-DE), CASEarth EarthDataMiner, Pixel Information Expert (PIE)-Engine, were analyzed from the aspects of public data achieve, platform type, and APIs. Meanwhile, the RS-CCP developed by Chinese Business Company were also taken into account, such as SenseEarth, Analytical Insight of Earth (AI EARTH), WeEath. Furthermore, this study summarized the main applications of RS-CCPs in the field of Earth sciences according to Amani et al. (2020) and Tamiminia et al. (2020). Specifically, the RS-CCPs based applications published on Nature (and its series), Science (and its series) and Proceedings of the National Academy of Sciences of the United States of America (PNAS) were summarized as applications related to land cover/land use, vegetation changes, animal, climate change, Human social and economic activities.On this basis, the limitations of current RS-CCPs were discussed, such as (1) Limited storage and computing resources, (2) Some geospatial data types are not compatible, (3) Insufficient support for different projection formats, (4) Difficult to achieve calculation between pixels, (5) Not support mobile applications, (6) The typesetting and drawing module is not perfect. The key technologies and core issues that need to be resolved in the future were prospected. Subsequently, some recommendations were provide for the development of China’s RS-CCP: (1) Integration of multi-source data resources, especially domestic remote sensing data, (2) Guarantee the quality and reliability of domestic remote sensing data, (3) Promote a new data-driven geoscience research paradigm. With the increasing demand of human understanding of the Earth, RS-CCPs will play a greater role in Earth science, serving the deepening of Earth science knowledge and the sustainable development of human society.