## 1 Introduction
Anyway, additional information and/or local measurements are still required for estimating the parameters of the simulation models [17]. On the other hand, remote sensors on board satellites are able to acquire thermal infrared data from which it is possible to retrieve the urban Land Surface Temperature (LST), allowing thus the study of the SUHI [18]. The major advantage of the SUHI is that it can be calculated easily for a large number of cities across large spatial domains. This allows to investigate the role of urbanization in influencing the SUHI over large regions [19; 20; 21; 22; 23; 24; 25; 26]. A wide literature about the determination of the urban LST is therefore available, based on remote sensing data and its connection with SUHI. At the same time, though, it is not easy to handle and process the huge amount of Earth Observation (EO) data actually available because of the continuously increasing number of sensors,spatial and temporal resolutions and this will be even more evident in the near future with the development of the micro-satellite constellations. As a matter of fact, even today, much of the archived EO imagery is underutilized despite modern computing and analysis infrastructures, mainly due to processing limitations when faced with a huge amount of data on standard computers and of the high technical expertise needed to use traditional supercomputers or large-scale commodity cloud computing resources [27]. In the present era of Geo Big Data, new computing instruments to support remote sensing investigations are therefore necessary, in order to fully exploit and make available the information content of the acquired data. This work is precisely included in this background: the aim is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free EO Big Data (i.e., Landsat and Sentinels' imagery) for long-term spatio-temporal monitoring SUHI and its connection with the LC changes. GEE is indeed the computing platform recently released by Google \"for petabyte-scale scientific analysis and visualization of geospatial datasets\". Through a dedicated High Performance Computing (HPC) infrastructure, it enables researchers to easily and quickly access more than thirty years of free public data archives, including historical imagery and scientific datasets, for developing global and large-scale remote sensing applications. In this way, many of the limitations related to the data downloading, storage and processing, which usually occur when such a large amount of Geo Big Data is analyzed, are effortlessly overcome [28; 29]. These features have attracted the attention of researchers from different disciplines, which have integrated GEE into several third-party applications [28]. Among these, Climate Engine (CE) [30] can play an important role in the field of large-scale climate monitoring: it is a free web application powered by GEE that enables users to process, visualize, download and share several global and regional climate and remote sensing datasets and products in real time. Therefore, by continuing the work started in [31], a general methodology for the large-scale monitoring of the SUHI was implemented through the joint use of GEE with CE and tested on six different U.S. cities. The work is organized as follows. In Section 2, an overview of the EO Big Data context is given, paying specific attention to the most promising analysis platforms and underlining the reasons why GEE was selected among them. In Section 3, the proposed methodology is described in detail. A large-scale spatio-temporal procedure was implemented thanks to GEE, also benefiting from the already established CE tool, to extract the LST from Landsat imagery, and a simple indicator was introduced to represent the SUHI evolution and its dependence on LC changes (Figure 1). Figure 1: The workflow of the methodology.