随着遥感大数据时代的到来，为快速处理和分析海量遥感数据，国内外涌现了众多遥感云计算平台，使得全球尺度、长时间序列遥感数据的快速分析和应用成为可能。本文在分析国内外遥感云计算平台现状的基础上，针对大数据时代国内缺少功能完备的遥感云计算平台，且国外遥感云计算平台对国产卫星数据支持不足等问题，基于容器云技术，构建了包含国产卫星数据且集数据、算力和技术于一体的时空遥感云计算平台PIE（Pixel Information Expert）-Engine Studio，实现了脚本驱动的遥感数据的按需获取以及海量数据的快速处理。采用Landsat 8数据，以生长季植被指数NDVI（Normalized Difference Vegetation Index）的计算为例，对比了本平台与GEE（Google Earth Engine）的数据处理能力。结果表明，由于计算资源的限制，本平台的计算和导出时间均比GEE稍长，但计算结果的空间分布一致，其中近68%的值均分布在（0.48，0.77），且二者差值的95.33%集中在（-0.13，0.13），结果较为可信。因此，本文构建的基于共享、开放的中国自主遥感云计算平台PIE-Engine Studio，可为地球科学领域的研究提供数据和算力支持，将有助于推进中国遥感云计算平台的发展进程，推动国产卫星数据在云计算平台上的应用。

With the arrival of remote sensing big data era, numerous remote sensing cloud computing platforms have emerged inland and overseas to rapidly process and analyze massive remote sensing data. The emergence of remote sensing cloud computing platform makes it possible to quickly analyze and apply remote sensing data on a global scale or for longterm sequences. However, currently, there is lacking of remote sensing cloud computing platform with complete functions in domestic, while foreign remote sensing cloud computing platform has insufficient support for domestic satellite data. Based on this situation, we have independently developed a spatiotemporal remote sensing cloud computing platform, PIE (Pixel Information Expert) -Engine Studio. By adopting container cloud technology, this platform integrating data, computing power and technology, can implements on-demand acquisition of remote sensing data and rapid processing of massive data just driven by the script. (1) This study first introduced the system architecture of PIE-Engine Studio, and then described the data storage and access mode. (2) PIE-Engine Studio provides operations for multiple objects such as number, matrix, image, vector, list, dictionary, etc., also machine learning algorithms and some special satellite algorithms. (3) Furthermore, this study illustrated the calculation flow of the platform in detail. Firstly, the user writes a script in the front-end to describe the calculation process of remote sensing data. Click the “Run” button, these codes automatically build the preliminary chained structure call syntax tree. Then the syntax tree is optimized in the back-end through filter the invalid calculation content. The computing tasks are then distributed to the computing services on multiple nodes through the scheduling center. Finally, the resulting visual map layer or data file is returned to the front-end interface triggered by specific front-end requests or operators (print, addLayer, export).(4) At last, an application case is presented, we adopted Landsat 8 data and taking the calculation of Normalized Difference Vegetation Index (NDVI) in the growing season as an example, the calculation results and running time of this platform are compared with Google Earth Engine (GEE). The results show that, due to the limitation of computing resources, the running and export time of this platform are slightly longer than that of GEE, but the spatial distribution of calculation results is consistent, among which about 68% values are distributed between (0.48, 0.77), and 95.33% of the difference between the two results is concentrated between (-0.13, 0.13). It shows that the results are reliable. Therefore, the remote sensing cloud computing platform constructed by this paper, can provide data resources and computing power for research in the field of earth science, and will help promote the development of remote sensing cloud computing platform in China and the application of domestic satellite data in cloud computing platform.