satellite remote sensing and deep learning for aerosols prediction

Introduction/purpose: the paper presents a new state-of-the-art method that involves nasa satellite imagery with the latest deep learning model for a spatiotemporal sequence forecasting problem. satellite-retrieved aerosol information is very useful in many fields such as pm prediction or covid-19 transmission. the input data set was modal2_e_aer_od which presents global aot for every 8 days from terra/modis. the implemented machine learning algorithm was built with convlstm2d layers in keras. the obtained results were compared with the new cnn lstm model. methods: computational methods of machine learning, artificial neural networks, deep learning. results: the results show global aot prediction obtained using satellite digital imagery as an input. conclusion: the results show that the convlstm developed model could be used for global aot prediction, as well as for pm and covid-19 transmission.