Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

Abstract

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and -0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Retrieval of High-Resolution Aerosol Optical Depth (AOD) using Landsat 8 imageries over different LULC classes over a City along Indo-Gangetic Plain, India

“…Similarly, for vegetation, RMB is -2.57%, which is better than other land classes. This may be due to the fact that SREM's estimated surface re ectance over vegetation land was not overestimated as much as other land classes, which results in higher AOD values relative to other classes (Bilal et al, 2022). This study showcases the dependable performance of the AOD retrieval approach of SEMARA in a highly polluted city Kanpur, India.…”

“…10(a) and (d)). Because surface re ectance makes a relatively substantial contribution to the top-of-atmosphere re ectance under low aerosol conditions, inaccuracies in surface re ectance calculation can have a signi cant impact on the retrieval of AOD (Bilal et al, 2022). In contrast, for higher aerosol loading, the contribution of aerosol is relatively higher than the surface re ectance, which affects the AOD retrieval signi cantly (Bilal et al, 2021;Gupta et al, 2016).…”

“…In this study, the SEMARA technique(Bilal et al, 2022) was used to retrieve AOD utilizing data from the green band of Landsat 8 OLI. Radiative Transfer Model (RTM) equations were applied to LANDSAT imageries to recover AOD at spatial resolution of 30m.…”

Performance of MODIS Deep Blue Collection 6.1 Aerosol Optical Depth Products Over Indonesia: Spatiotemporal Variations and Aerosol Types

“…e slope and intercept between collocated MODIS AOD and AERONET AOD were calculated using the reduced major axis (RMA) method, which incorporates errors in both independent (AERONET) and dependent (MODIS) variables [36].…”