Discrimination Aerosol Form Clouds Using Cats-Iss Lidar Observations Based on Random Forest and SVM Algorithms Over the Eastern Part of Middle East

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.

Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

“…RF was also used for lidar measurements specifically. Brakhasi et al [17] used two algorithms, including RF, to discriminate aerosols from clouds in satellite-based lidar measurements, and they found the method had higher accuracy compared to probability distribution function-based algorithms. Liu et al [24] compared several machine learning algorithms on air pollution (SO 2 and NO 2 ) classification and found that RF can give a good result.…”