A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data

The improvement of the spatial and temporal resolution of reflectance data products has been challenging due to the diversity of data sources and availability of many data merging and fusion algorithms. In the algorithmic domain, methods for data merging and fusion may include, but are not limited to, the modified quantile-quantile adjustment (MQQA), the Bayesian maximum entropy (BME), and the spatial and temporal adaptive reflectance fusion model (STARFM). This article presents a synergistic integration of the data merging and fusion algorithms of MQQA and BME in dealing with heterogeneous and nonstationary surface reflectance data at both the top of atmosphere (TOA) and land surface for a comparative study. Emphasis has been placed on the distinctive performance between BME and MQQA-BME algorithms in the spatial domain and the MQQA-BME and STARFM in the temporal domain at both TOA and land surface levels. The results indicate that the BME and MQQA-BME outperform the MQQA in terms of the spatial coverage at both TOA and land surface levels. Moreover, the MQQA-BME algorithm shows a higher prediction accuracy than STARFM at the blue band over the temporal domain at both TOA and land surface levels. The results of this comparison will greatly empower the MQQA-BME to be used for urban air quality monitoring and related epidemiological assessment in the future, once finer aerosol optical depth predictions via integrated data merging and fusion can be made possible.