Publications

Shi, SY; Cheng, TH; Gu, XF; Guo, H; Chen, H; Wang, Y; Wu, Y (2018). Multisensor Data Synergy of Terra-MODIS, Aqua-MODIS, and Suomi NPP-VIIRS for the Retrieval of Aerosol Optical Depth and Land Surface Reflectance Properties. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 56(11), 6306-6323.

Abstract
A novel multisensor synergy method, using data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua as well as Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership, is presented to retrieve optical atmosphere-surface properties. By adopting three-sensor observations' synergy, the proposed method can grasp the multitemporal characteristics of aerosol optical depth (AOD) and the multidirectional characteristics of surface reflectance. In addition, the bidirectional reflectance distribution function (BRDF) can be derived at daily scale by adopting the novel shape function constrained BRDF retrieval (SFCBR) method. The 550-nm AOD retrieval result of the proposed method is validated by AErosol RObotic NETwork (AERONET) measurement at Beijing, XiangHe, Noto, and Gwangju_GIST sites with R-2 equaling to 0.78, 0.75, 0.70, and 0.75, respectively. Compared with MODIS/VIIRS AOD official product, the proposed method shows higher coverage rate (especially in AERONET Beijing site with approximately 50% increase) with comparative accuracy. The expected error of the retrieved AOD from the proposed method is estimated as Delta tau = +/--0.05 +/- 0.24 tau. The correlation coefficients of BRDF-derived albedo time series between the proposed method and MODIS BRDF/Albedo product can reach up to 0.85 with an obvious improvement in temporal resolution by adopting an SFCBR method. The average relative differences of BRDF shape function between the retrieval result and MODIS BRDF/Albedo product in all directions equal to 0.026, 0.036, 0.037, and 0.016 in AERONET Beijing, XiangHe, Noto, and Gwangju_GIST sites, respectively.

DOI:
10.1109/TGRS.2018.2835508

ISSN:
0196-2892