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Remote Sens. 2016, 8(12), 998; doi:10.3390/rs8120998

A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

1
Graduate School of Agriculture, Hokkaido University, Sapporo 0608589, Japan
2
Research Faculty of Agriculture, Hokkaido University, Sapporo 0608589, Japan
3
School of Geographical Sciences, Northeast Normal University, Changchun 130024, China
4
Faculty of Fisheries Sciences, Hokkaido University, Hakodate 0418611, Japan
5
Faculty of Agriculture, Kagawa University, Kagawa 7610795, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Alexander A. Kokhanovsky, Richard Müller and Prasad S. Thenkabail
Received: 22 September 2016 / Revised: 28 November 2016 / Accepted: 29 November 2016 / Published: 7 December 2016
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Abstract

In this paper, we introduced a new algorithm for retrieving aerosol optical depth (AOD) over land, from the Cloud and Aerosol Imager (CAI), which is one of the instruments on the Greenhouse Gases Observing Satellite (GOSAT) for detecting and correcting cloud and aerosol interference. We used the GOSAT and AErosol RObotic NETwork (AERONET) collocated data from different regions over the globe to analyze the relationship between the top-of-atmosphere (TOA) reflectance in the shortwave infrared (1.6 μm) band and the surface reflectance in the red (0.67 μm) band. Our results confirmed that the relationships between the surface reflectance at 0.67 μm and TOA reflectance at 1.6 μm are not constant for different surface conditions. Under low AOD conditions (AOD at 0.55 μm < 0.1), a Normalized Difference Vegetation Index (NDVI) based regression function for estimating the surface reflectance of 0.67 μm band from the 1.6 μm band was summarized, and it achieved good performance, proving that the reflectance relations of the 0.67 μm and 1.6 μm bands are typically vegetation dependent. Since the NDVI itself is easily affected by aerosols, we combined the advantages of the Aerosol Free Vegetation Index (AFRI), which is aerosol resistant and highly correlated with regular NDVI, with our regression function, which can preserve the various correlations of 0.67 μm and 1.6 μm bands for different surface types, and developed a new surface reflectance and aerosol-free NDVI estimation algorithm, which we named the Modified AFRI1.6 algorithm. This algorithm was applied to AOD retrieval, and the validation results for our algorithm show that the retrieved AOD has a consistent relationship with AERONET measurements, with a correlation coefficient of 0.912, and approximately 67.7% of the AOD retrieved data were within the expected error range (± 0.1 ± 0.15AOD(AERONET)). View Full-Text
Keywords: AOD retrieval; GOSAT CAI; Modified AFRI1.6 algorithm; surface reflectance AOD retrieval; GOSAT CAI; Modified AFRI1.6 algorithm; surface reflectance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhong, G.; Wang, X.; Tani, H.; Guo, M.; Chittenden, A.R.; Yin, S.; Sun, Z.; Matsumura, S. A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data. Remote Sens. 2016, 8, 998.

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