Data fusion algorithms help extract information from “asynchronous” time series satellite data whereas data merging data help extract information from “synchronous” time series satellite data into a series of synthetic images by using the temporal, spatial, or even spectral properties. Such data fusion algorithms including Bayesian maximum entropy (BME) and spatial and temporal adaptive reflectance fusion model (STARFM) have greatly improved the coverage, enhancing data application potential with higher spatiotemporal resolution via multi-sensor earth observations. The goal of this study is to assess the utility of BME and modified BME algorithm with the aid of a data merging algorithm called Modified Quantile-Quantile Adjustment (MQQA), in comparison with STARFM for the retrieval of Aerosol Optical Depth in an urban environment. MQQA heavily counts on big data to support the systematic bias correction from “synchronous” time series satellite data. Such assessment of algorithmic efficiency needs to be carried out for both top of atmosphere reflectance and ground reflectance levels in support of the deep blue method for the retrieval of atmospheric optical depth at the ground level.

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11130, Imaging Spectrometry XXIII: Applications, Sensors, and Processing, 1113007 (6 September 2019); doi: 10.1117/12.2526790

Published in SPIE Proceedings Vol. 11130:
Imaging Spectrometry XXIII: Applications, Sensors, and Processing
Emmett J. Ientilucci, Editor(s)