Despite the computing power growth of the cloud-based platforms providing the opportunity to process the big amounts of remote sensing data, there is still a need to process and store some kinds of data locally. During the last several years, scientists have been interested in makings to make the popular open-source GIS application QGIS and the most popular cloud-based platform Google Earth Engine (GEE) friends. However, today it is not comfortable to use different processing platforms because of their data format differences or some long converting data manipulations. Some platforms suggest their cloud-based storage services use, but some restrictions include limited storage space, upload/download speed limitations, or data format differences. This research uses the GEE as a well-designed platform with often updated remote sensing data storage, processing tools, and algorithms for interdisciplinary research using the cloud and desktop data processing opportunities. The main idea is to create a well-designed infrastructure that can be more flexible than separately used cloud and desktop platforms. An integrated cloud-desktop processing infrastructure is based on GEE cloud performance via the Python API, QGIS as a powerful desktop GIS software, and PostgreSQL with PostGIS extension to store and analyze the results of cloud-desktop processing of remote sensing data locally by database management system (DBMS). Integration of the cloud-desktop processing has allowed for increasing the data processing speed, ensuring the operational usage of the representative satellite observation data, and processing long observation series without using local capacities without needs.