## 1 Introduction  
Remote Sensing (RS) refers to the technique of observing atmospheric objects remotely. Conventionally, RS was used for satellite and airborne platforms, obtaining data from optical and radar sensors [1]. Formerly, more than 3000 satellites in orbit were used in several applications. These satellites are equipped with various instruments within different temporal, spatial, and spectral resolutions oscillating from low to high. Satellites' sensors measure variables and then diffuse data into ground data centers over downlink networks [2]. The significant growth of industrial, transport, and agricultural activities has directed many environmental matters, notably outdoor Air Pollution (AP) [3]. Therefore, AP can excessively disturb human health and cause climate change. For this purpose, Air Quality (AQ) now merits special consideration from many scientific communities [4]. Continuous AQ monitoring is one of the propositions helping decision-makers [5]. It's a Near-Real-Time (NRT) monitoring of Aerosol Optical Depths (AOD), offers obstinate input data for AQ models, and tracks the pollutant plumes emitted from industrial and agricultural sources [6]. The acquired data are stored in a complicated scientific file format precisely: The Binary Universal Form for the Representation of meteorological data (BUFR), the Network Common Data Form (NetCDF), the Hierarchical Data Format (HDF5), and so on. The daily size of the downloaded RS data is approximately 55 gigabits (GB) and sums up to 17 terabits (TB) per year [7]. Additionally, the velocity with which information is transmitted is fast, with a rate of 40,000 files per day. Hence, RS data are complex, have huge volumes, high velocity, and veracity, confirming that satellite data are BD. So, the processing is complicated and takes a long execution time, and the existing platforms for RS data processing are limited and face many challenges [8]. Two main approaches are used to deal with RS data. Firstly, satellite images are generally processed with software and libraries like ENVI, Geographic Information System (GIS) programs, or other image processing algorithms. This method is excellent with optical sensors. However, it cannot deal with other scientific file formats, notably the NetCDF, HDF5, Binary (BIN), GRIB, etc. It is also less efficient because it does not support multisource data with high velocity, veracity, and huge volume. Thus, the limitation is evident: the processing does not support Remote Sensing Big Data (RSBD), which is not compatible with distributed and scalable computing. Secondly, RS data can be pre-processed in batch processing software for ingestion and then integrated into a scalable framework for processing, such as Hadoop for extra processing. This method is more efficient because it supports RSBD and makes them semi-structured and compatible with MapReduce (MR) and Structured Query Language (SQL) languages. It could also be run in a distributed and scalable cluster to optimize the execution time and keep its freshness. In our previous works, we achieved many pieces of research to identify the nature and the features of the used satellite data. We also proposed SAT-ETL-Integrator: BD batch processing ingestion software for RSBD. The suggested ingestion tool acquires, decompresses, filters, converts, and extracts refined datasets from massive RSBD input. JAVA, Python, and Shell were the fundamental programming languages used in the development. We also came up with SAT-CEP-Monitor, innovative software for RS data processing in streaming using Complex Event Processing (CEP). This work aimed to benefit from the strengths of the previous proposal, including cloud and Hadoop technologies and to optimize the execution time, guarantee the NRT aspect for some Earth Observation (EO) applications, and integrate the RS data in Hadoop to apply Artificial Intelligence (AI) models for prediction.