Next Article in Journal
Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing
Previous Article in Journal
Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(4), 322; doi:10.3390/rs8040322

Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing

1
Department of Atmospheric Sciences, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
2
Earth Observation Science Group, Department of Physics & Astronomy, University of Leicester, University Road, Leicester LE1 7RH, UK
3
National Centre for Earth Observation, University of Leicester, University Road, Leicester LE1 7RH, UK
4
Department of Spatial Information Engineering, Pukyong National University, 45, Yongso-ro, Nam-gu, Pusan 48513, Korea
5
National Institute of Meteorological Sciences, 33, Seohobuk-ro, Seogwipo-si, Jeju 63568, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 5 January 2016 / Revised: 25 March 2016 / Accepted: 4 April 2016 / Published: 12 April 2016
View Full-Text   |   Download PDF [3784 KB, uploaded 12 April 2016]   |  

Abstract

Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO2 retrieval errors and averaging kernels. The a posteriori XCO2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (<1.3 ppm) over vegetation surfaces. Column averaging kernels are generally close to unity near the surface and decrease with increasing altitude. For dust aerosol with an AOD of 0.3, the retrieval loses its sensitivity near the surface due to the influence of atmospheric scattering, with the peak of column averaging kernels at ~800 hPa. In addition, we performed a sensitivity analysis of the principal state vector elements with respect to XCO2 retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO2 retrievals work reasonably well. The XCO2 retrieval errors with respect to state vector elements are shown to be <0.3 ppm. Information on aerosol optical properties is the most important factor affecting the XCO2 retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO2 retrievals of up to 2.5 ppm. The XCO2 retrievals using the Thermal and Near-infrared Sensor for carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) L1B spectra were biased by 2.78 ± 1.46 ppm and 1.06 ± 0.85 ppm at the Saga and Tsukuba sites, respectively. This study provides important information regarding estimations of the effects of aerosol properties on the CO2 retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO2 retrievals, especially combined with an aerosol retrieval algorithm. View Full-Text
Keywords: CO2 retrieval; GOSAT; aerosol; FTS CO2 retrieval; GOSAT; aerosol; FTS
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jung, Y.; Kim, J.; Kim, W.; Boesch, H.; Lee, H.; Cho, C.; Goo, T.-Y. Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing. Remote Sens. 2016, 8, 322.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top