Retrieval of Urban Aerosol Optical Depth from Landsat 8 OLI in Nanjing, China

Yangyang Jin,Dong He,Jian Chen,Zhihua Mao,Delu Pan,Zengzhou Hao,Qingjiu Tian

doi:10.3390/rs13030415

Abstract

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of the aerosol optical depth (AOD) is of great significance in climate research and environmental protection. Based on Landsat 8 Operational Land Imager (OLI) images and MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved the AODs in Nanjing City from 2017 to 2018 using the combined Dark Target (DT) and Deep Blue (DB) methods. The retrieval accuracy was validated by in-situ CE-318 measurements and MOD04_3K aerosol products. Furthermore, we analyzed the spatiotemporal distribution of the AODs and discussed a case of high AOD distribution. The results showed that: (1) Validated by CE-318 and MOD04_3K data, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the retrieved AODs were 0.874 and 0.802, 0.134 and 0.188, and 0.099 and 0.138, respectively. Hence, the combined DT and DB algorithms used in this study exhibited a higher performance than the MOD04_3K-obtained aerosol products. (2) Under static and stable meteorological conditions, the average annual AOD in Nanjing was 0.47. At the spatial scale, the AODs showed relatively high values in the north and west, low in the south, and the lowest in the center. At the seasonal scale, the AODs were highest in the summer, followed by spring, winter, and autumn. Moreover, changes were significantly higher in the summer than in the other three seasons, with little differences among spring, autumn, and winter. (3) Based on the spatial and seasonal characteristics of the AOD distribution in Nanjing, a case of high AOD distribution caused by a large area of external pollution and local meteorological conditions was discussed, indicating that it could provide extra details of the AOD distribution to analyze air pollution sources using fine spatial resolution like in the Landsat 8 OLI.

Highlights

Where ρ TOA is the apparent reflectance at TOA; ρ0 is the atmospheric path radiation reflectance; θS, θV, and φ are solar zenith angle (SZA), view zenith angle (VZA), and relative azimuth angle (AZ), respectively; T and T are atmospheric transmittance on the path for Solar–Surface and Surface–Sensor, respectively; S is the spherical albedo of the atmosphere; and ρS is the surface reflectance
The 19-d Landsat 8 satellite images covering the region of Nanjing were selected from 2017 to 2018 for aerosol optical depth (AOD) retrieval with the combination of the Dark Target (DT) and Deep Blue (DB) algorithms
The results indicated that the combined algorithms could simultaneously invert the spatial distribution of aerosols in the dark-pixel and bright-surface areas, with good continuity

Summary

Introduction

With the rapid development of cities and the acceleration of industrialization processes, the problem of air pollution is becoming more and more serious [1]. High aerosol particles reduce the air quality of human living environments [2] and even affect the health of humans [3]. Many ground observing stations, such as air quality monitoring networks [4], have been built to monitor air quality by getting PM2.5 and/or PM10 aerosol particles [5]. It is difficult to fully grasp the large-scale distribution of aerosol particles and even monitor the source and movement of aerosol pollution from limited ground stations [6]. Satellite remote sensing has the advantage of broad

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Results

Discussion

Conclusion