This research aims to monitor abnormal climate changes and supervise Air Quality (AQ), especially in Morocco. This study aims to contribute to finding a solution to the AQ degradation and climate change issues by using Remote Sensing (RS) techniques. RSBD in NRT is collected from six sources: the MDEO ground station of EUMETSAT data, the EOSDIS data of NASA, the NESDIS data of NOAA, and the Copernicus platform, some MGS data, and the Raspberry PI sensors data. The current manuscript explains the different aspects of the used satellite data, proving that satellite data could be regarded as Big Data (BD). Accordingly, this research has proposed a Hadoop BD architecture and explained how to efficiently process RS environmental data. This architecture comprises six main layers: the data sources, data ingestion, data storage, data processing, data visualization, and the monitoring layer. The aforementioned architecture automatically collects filters, extracts, and stores data into the HDFS. This proposed model would be beneficial in managing adverse climate conditions and prevent natural disasters.

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