## 1 Introduction  
In recent years, with the rapid advancement of satellite remote sensing technology, the number of satellites launched by various countries across the world has been increasing year by year [1]. Low- and medium-resolution satellites used for ocean, atmosphere, environment, social welfare, and scientific research form a large proportion among them at the regional to global scale [2]. Research on massive remote sensing images utilizing deep learning models plays a significant part in urban planning [3, 4], environmental observations [5, 6], and energy development [7], especially with the recent increasing interest. The collection of data is time-consuming, labor-intensive, and difficult. The amount of data is undergoing explosive growth and increasing at a petabyte level. Taking the Landsat 8 OLI/TIRS Level-2 data type in the United States Geological Survey (USGS) [8] as an example, the China region alone generated about 1500 remote sensing data products per month, each with a data capacity of 2G, in 2019. Remote sensing technology has been unprecedentedly improved in terms of data acquisition capabilities and information service capabilities. In order to realize the sharing service of satellite remote sensing data, the National Aeronautics and Space Administration (NASA) [9] and European Space Agency (ESA) [10] have established remote sensing data-sharing portals for the above public satellites and provide each registered user with equal access to remote sensing data. Therefore, these data-sharing portals have gradually become a popular data source of large amounts of acquired remote sensing data for individuals, small, medium, and micro enterprises, and scientific research institutions. Although these data-sharing portals provide each registered user with an equal opportunity to obtain remote sensing data, the limitations on the access to IP, frequency, and bandwidth are becoming challenges in the large-scale acquisition of remote sensing data for users. Google Earth Engine (GEE) is a very useful resource for large-scale remote sensing data applications [11; 12]. Although it is free for research projects, it is limited in capacity for data export. It is also not suitable for uploading private datasets to GEE for processing [13]. GEE cannot satisfy large-scale remote sensing scientific research. Many remote sensing research activities, including remote sensing observations and data assimilation for Earth system models, are not supported by GEE. The rapid development of cloud technologies [14] can be used for processing and integrating large-scale, heterogeneous resources, which provides further opportunities for large-scale massive remote sensing data acquisition. Early work on task scheduling with parallel computing was conducted by [PERSON] in 1989 [15]. Task scheduling has always been a hot research topic in project production and scientific research, and it is also a major research area of interest to researchers. This type of problem is ultimately a non-deterministic polynomial problem (NP) for task scheduling. Generally, task scheduling algorithms consist of traditional task scheduling and meta-heuristic algorithms [16]. Traditional task scheduling algorithms include First Come First Serve (FCFS) [17], Shortest Job First (SJF) [17], Max-Min [18], Min-Min [19], and polling scheduling [20; 21]. [PERSON] et al. [22] modified standard heuristics for task assignment in predictable environments. Meta-heuristic task scheduling algorithms include Particle Swarm Optimization (PSO) [23; 24], a genetic algorithm (GA) [25], an ant algorithm [26], BP algorithms [27; 28], and an adaptive meta-heuristic-based method [29]. Recently, some studies have focused on improved hybrid algorithms based on the above-mentioned algorithms. [PERSON] et al. [30] proposed a novel hybrid algorithm that combines SJF and Round Robin (RR) schedulers considering a dynamic variable task quantum.