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Journal of Applied Remote Sensing

High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: application and initial validation
Author(s): Yuhuan Zhang; Zhengqiang Li; Ying Zhang; Weizhen Hou; Hua Xu; Cheng Chen; Yan Ma
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Paper Abstract

The Geostationary Ocean Color Imager (GOCI) provides multispectral imagery of the East Asia region hourly from 9:00 to 16:00 local time (GMT+9 ) and collects multispectral imagery at eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm) with a spatial resolution of 500 m. Thus, this technology brings significant advantages to high temporal resolution environmental monitoring. We present the retrieval of aerosol optical depth (AOD) in northern China based on GOCI data. Cross-calibration was performed against Moderate Resolution Imaging Spectrometer (MODIS) data in order to correct the land calibration bias of the GOCI sensor. AOD retrievals were then accomplished using a look-up table (LUT) strategy with assumptions of a quickly varying aerosol and a slowly varying surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of a series of observations in a short period of time, such as several days. The monitoring of hourly AOD variations was implemented, and the retrieved AOD agreed well with AErosol RObotic NETwork (AERONET) ground-based measurements with a good R2 of approximately 0.74 at validation sites at the cities of Beijing and Xianghe, although intercept bias may be high in specific cases. The comparisons with MODIS products also show a good agreement in AOD spatial distribution. This work suggests that GOCI imagery can provide high temporal resolution monitoring of atmospheric aerosols over land, which is of great interest in climate change studies and environmental monitoring.

Paper Details

Date Published: 20 June 2014
PDF: 16 pages
J. Appl. Remote Sens. 8(1) 083612 doi: 10.1117/1.JRS.8.083612
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Yuhuan Zhang, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Zhengqiang Li, Institute of Remote Sensing and Digital Earth (China)
Ying Zhang, Institute of Remote Sensing and Digital Earth (China)
Weizhen Hou, Institute of Remote Sensing and Digital Earth (China)
Hua Xu, Institute of Remote Sensing and Digital Earth (China)
Cheng Chen, Institute of Remote Sensing and Digital Earth (China)
Yan Ma, Institute of Remote Sensing and Digital Earth (China)


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