Quality assurance plan for China collection 2.0 aerosol datasets

She, Lu, Xue, Yong, Guang, Jie, He, Xingwei and Li, Chi (2014) Quality assurance plan for China collection 2.0 aerosol datasets. In: 2014 IEEE Geoscience and Remote Sensing Symposium, 13-18 July 2014, QUÉBEC CITY, CANADA.

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Abstract / Description

The inversion of atmospheric aerosol optical depth (AOD) using satellite data has always been a challenge topic in atmospheric research. In order to solve the aerosol retrieval problem over bright land surface, the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm has been developed based on the synergetic using of the MODIS data of TERRA and AQUA satellites [1, 2]. In this paper we describe, in details, the quality assessment or quality assurance (QA) plan for AOD products derived using the SRAP algorithm. The pixel-based QA plan is to give a QA flag to every step of the process in the AOD retrieval. The quality assessment procedures include three common aspects: 1) input data resource flags, 2) retrieval processing flags, 3) product quality flags [3]. Besides, all AOD products are assigned a QA ‘confidence’ flag (QAC) that represents the aggregation of all the individual QA flags. This QAC value ranges from 3 to 0, with QA = 3 indicating the retrievals of highest confidence and QA = 2/QA = 1 progressively lower confidence [4], and 0 means ‘bad’ quality. These QA (QAC) flags indicate how the particular retrieval process should be considered. It is also used as a filter for expected quantitative value of the retrieval, or to provide weighting for aggregating/averaging computations [5]. All of the QA flags are stored as a “bit flag” scientific dataset array in which QA flags of each step are stored in particular bit positions.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Aerosol retrieval, MODIS, Quality assessment, QA flag, Scientific dataset
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 25 Feb 2019 09:45
Last Modified: 25 Feb 2019 09:45
URI: http://repository.londonmet.ac.uk/id/eprint/4675

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