Abstract
Abstract. Aerosols and Clouds play an important role in the Earth's environment, climate change and climate models. The Cloud-Aerosol Transport System (CATS) as a lidar remote sensing instrument, from the International Space Station (ISS), provides range-resolved profile measurements of atmospheric aerosols and clouds. Discrimination aerosols from clouds have always been a challenges task in the classification of space-born lidars. In this study, two algorithms including Random Forest (RF) and Support Vector Machine (SVM) were used to tackle the problem in a nighttime lidar data from 18 October 2016 which passes form the western part of Iran. The procedure includes 3 stages preprocessing (improving the signal to noise, generating features, taking training sample), classification (implementing RF and SVM), and postprocessing (correcting misleading classification). Finally, the result of classifications of the two algorithms (RF-SVM) were compared against ground truth samples and Vertical Feature Mask (VFM) of CATS product indicated 0.96–0.94 and 0.88–0.88 respectively. Also, it should be mentioned that a kappa accuracy 0.88 was acquired when we compared VFM against our ground truth samples. Moreover, a visual comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and RGB products demonstrating that clouds and aerosol can be well detected and discriminated. The experimental results elucidated that the proposed method for classification of space borne lidar observation leads to higher accuracy compared to PDFs based algorithms.
Highlights
Aerosols are suspended particles in the atmosphere (Wong et al, 2013) which have a different physical, chemical and light scattering characteristic and so they have a high variability in time and space (IPCC, 2001; Murari et al, 2015; Tomasi et al, 2015)
Color ratio are related to the size of the particles and value 1 or less is related to aerosols particle and value greater than 1 is related to clouds (Fig.3 (b)), while depolarization ratio is an index of shape of the particles
This study presents performance of two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), on CAST-International Space Station (ISS) lidar observation
Summary
Aerosols are suspended particles (liquid or soil) in the atmosphere (Wong et al, 2013) which have a different physical, chemical and light scattering characteristic and so they have a high variability in time and space (IPCC, 2001; Murari et al, 2015; Tomasi et al, 2015). These particles are produced as the result of natural activities such as volcanoes, storms as well as human activities such as burning fossil fuels and traffic are produced (Gong and Ma, 2012; Kokkalis et al, 2012; Wiltshire, 2011; Zhu et al, 2016). It should be mentioned that classification and discrimination of aerosols from clouds as the most important
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