Mehdi Amiri; Saif ollah Soleimani; Fakhteh Soltani Tafreshi
Abstract
Dust storm increased in both spatial and temporal aspects during last decade. Middle East dust storms have caused countless social, economic and environmental damages for the residents of South and Southwest regions of Iran. MODIS satellite imagery has certain advantages, including available and useful ...
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Dust storm increased in both spatial and temporal aspects during last decade. Middle East dust storms have caused countless social, economic and environmental damages for the residents of South and Southwest regions of Iran. MODIS satellite imagery has certain advantages, including available and useful spectral bands, with high spatial and radiation resolution and MODIS data are used in the present study. In this study, two MODIS datasets were used. Part one, model development data (January 18-21, 2018) and part two, model evaluation data. Metrological data are collected with respect to time interval studied. After preprocessing MODIS data and preparing field observations, features (artificial neural network input) were generated by proposed method from MODIS data. A model through artificial neural network analysis was developed. This model extracts dust storm and estmates visibility. Model outputs were compared visually with NDDI outputs.To evaluate the effectiveness of the proposed method, the developed model was tested with other time data. Model outputs were compared visually with NDDI outputs. Eventually, in order to reveal the strengths and weaknesses of the proposed method, an accuracy assessment has been carried out by comparing the models output with visibility parameter of synoptic stations. The observation root mean squared error are10%, 10%, 15% and 10% related to January 18th, January 19th, January 20th and 21th, and also, 20% and 25% related to January 26th, 2019 and October 28th, 2018, respectively.
