Toward Developing an African Earth Open Portal of Remote Sensing Data Processing for Environmental Monitoring Applications

Abstract

    Morocco has become one of the most industrialized feet, not only in Africa but also in the world. This mutation benefits the country's development; however, these industrial activities directly or indirectly impact the impairment grade environment. Remote Sensing (RS) data offer consideration from government projects and commercial applications to academic fields from free-open access data centers. The EUMETSAT NASA, NOAA, ESA, Copernicus, etc., deliver RS data with numerous satellites flying on geostationary and polar orbits. The provided data are massive (Terabytes daily) for environmental monitoring, disaster management, and other applications. RS products are measured with various instruments, for instance, radiometer, spectrometer, hyperspectral, sounder, altimeter, and optical. Wide spectral bands are employed, such as infrared, visible, radar, microwave, etc. The proliferation of RS data also increases the RS data's velocity (thousands of files daily), the data's diversity (NetCDF, HDF5, BUFR, binary, etc.), and higher dimensionality characteristics. Accordingly, RS data can be regarded as Big Data (BD). Thus, it is challenging to acquire, ingest, process, store, query, and visualize RSBD proficiently due to the data and computing-intensive challenges and limitations. As a result, an incredible deal of attention in the field of BD and its analysis has increased, most ambitious from a vast number of research challenges powerfully related to RS applications, such as modeling, pre-processing, analyzing, querying, and mining, in distributed and scalable clusters.    This project aims to design and develop an African Earth Open Portal (AfEOP) as a platform for the automatic acquisition, ingestion, processing, and visualization of the massive stream of RS datasets from multiples satellites sensors, ground stations, drones, etc. The proposed platform will solve many environmental issues, notably: (1) supervising the weather parameters in Africa, including the temperature, humidity, pressure, and wind speed, etc. (2) drought assessment, evapotranspiration estimation, water drainage monitoring, and food yield and crop forecasting 3) agriculture and fertilization optimization using Artificial Intelligence (AI) algorithms helping in decision making 4) water use reduction using RS data, AI, and physical models.    In this project, we perform BD Analytics by (1) presenting a brief survey of the used data sources and describing the nature of the used satellite sensors' data for environmental applications. (2) designing and developing an ingestion framework for RSBD in a distributed platform for RS data storage and query. (3) incorporating cloud computing and parallel programming techniques to optimize processing. (4) visualizing the results in demand in interactive maps and dashboards. Accordingly, this led us to the following questions: Is the designed architecture of RS data pre-processing efficient to extract only helpful information in a short execution time? Is it possible to make the RS data-friendly with the distributed framework for more processing? Are RS techniques efficient for environmental applications for Africa and notably Morocco?