Publications

Zhang, TH; Shen, HF; Xia, XH; Wang, LC; Mao, FY; Yuan, QQ; Gu, Y; Zhao, B; Zhu, ZM; Lin, Y; Bo, YC; Gong, W (2023). Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 61, 4101615.

Abstract
High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellites possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments of their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by a relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, most of them aim at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with the sun-synchronous orbit (SSO) satellite observations based on the Bayesian maximum entropy (BME) theorem is developed for GEO Himawari-8 Advanced Himawari Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5%to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other GEO satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

DOI:
10.1109/TGRS.2023.3262785

ISSN:
1558-0644