China collection 2.1: Aerosol Optical Depth dataset for mainland China at 1km resolution

Xue, Yong, He, Xingwei, Xu, Hui, Guang, Jie, Guo, Jianping and Mei, Linlu (2015) China collection 2.1: Aerosol Optical Depth dataset for mainland China at 1km resolution. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 26-31 July 2015, Milan, Italy.

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Abstract / Description

A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 6.0 with spatial resolution of 3km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved AOD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.1 at 1km spatial resolution, dated from August 2002 to 2012. We compared the China Collection 2.1 AOD datasets for 2010 with AERONET data. From those 2460 collocations, representing mutually cloud-free conditions, we find that 62% of China Collection 2.1 AOD values comparing with AERONET-observed values within an expected error envelop of 20% and 55% within an expected error envelop of 15%. Compared with MODIS Level 2 aerosol products, China Collection 2.1 AOD datasets have a more complete coverage with fewer data gaps over the study region.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Aerosol Optical Depth (AOD), Synergetic Retrieval of Aerosol Properties (SRAP), Moderate Resolution Imaging Spectroradiometer (MODIS), China Collection 2.1
Subjects:	000 Computer science, information & general works
Department:	School of Computing and Digital Media
Depositing User:	Bal Virdee
Date Deposited:	21 Feb 2019 16:22
Last Modified:	21 Feb 2019 16:22
URI:	https://repository.londonmet.ac.uk/id/eprint/4638

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