Publications

Tian, XP; Liu, Q; Song, ZW; Dou, BC; Li, XH (2018). Aerosol Optical Depth Retrieval From Landsat 8 OLI Images Over Urban Areas Supported by MODIS BRDF/Albedo Data. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 15(7), 976-980.

Abstract
This letter presents a new algorithm that allows the retrieval of the aerosol optical depth (AOD) at a high (500 m) spatial resolution from Landsat 8 Operational Land Imager (OLI) data over urban areas. Because of the complex structure over urban surfaces, the bidirectional reflectance characteristic is obvious; however, most of the current aerosol retrieval algorithms over land do not account for the anisotropic effect of the surface. This letter improves the quality of AOD retrieval by providing the surface reflectance based on the multiyear MODIS bidirectional reflectance distribution function (BRDF)/Albedo model parameters product (MCD43A1) and the RossThick-LiSparse reciprocal kernel-driven BRDF model. The ground-based Aerosol Robotic Network (AERONET) AOD measurements from five sites located in urban and suburban areas are used to validate the AOD retrievals, and the MODIS Terra Collection 6 (C6) dark target/deep blue AOD products (MOD04) at 10-km spatial resolution are obtained for comparison. The validation results show that the AOD retrievals from the Oil images are well correlated with the AERONET AOD measurements (R = 0.987), with a low root-mean-square error of 0.07, a mean absolute error of 0.036, and a relative mean bias of 1.029; approximately 95.3% of the collocations fall within the expected error. The analysis indicates that the BRDF is essential in ensuring the accuracy of AOD retrieval. Compared with the MOD04 AOD retrievals, the OLI AOD retrievals have better spatial continuity and higher accuracy. The new algorithm can provide continuous and detailed spatial distributions of the AOD over complex urban surfaces.

DOI:
10.1109/LGRS.2018.2827200

ISSN:
1545-598X