Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

Abstract

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

“…Himawari-8 is a geostationary weather satellite launched by JMA in 2014 and positioned at 140.7 • E. The AHI onboard Himawari-8 has a spatial coverage of 150 • by 150 • centered at 0 • N 140.7 • E. The AHI images have 6 solar reflective spectral bands, including 3 visible and 3 near-infrared bands with spatial resolutions of 0.5 km to 2 km at the sub-satellite point for different bands. These 6 bands enabled AOD retrieval capability that has been shown in many sensors with similar band configurations such as Landsat TM/ETM+/OLI [23] and MODIS [24]. The AHI provided a full-disk observation every 10 min.…”

Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China

“…The limited number of images, further restricted by the temporal resolution, may impose some negative influence on the results. This limitation is also widely seen in other AOD researches based on satellites with high spatial but low temporal resolutions such as Landsat-8 and Sentinel-2 [59,61,62]. Although GF-1 has a medium-length repetition cycle of four days, which is shorter than that of Landsat-8 (16 days) and Sentinel-2 (10 days), it may be still insufficient for regions that experience frequent cloudy conditions or rainy weather such as Shenzhen City.…”

“…Firstly, it is the influence of aerosol transportation. Hoff and Christopher [15] and Li et al [59] considered the movement of aerosol as an important factor when they investigated the proper window size for AOD to match in situ AOD or PM 2.5 measurements. Therefore, it can be reasonably speculated that in case of the continuous monitoring and aerosol transportation, the value of in situ PM 2.5 measurements should be not only a temporal average, but also a spatial average at a certain scale.…”

Global Evaluation of the Suitability of MODIS-Terra Detected Cloud Cover as a Proxy for Landsat 7 Cloud Conditions

“…Several factors can contribute to differences among satellite cloud products. Wind can displace clouds and other atmospheric constituents in the time between different satellite overpasses [16,17]. Clouds evolve over time, and in many parts of the world, cloud cover increases during the day over land [18].…”