Paper
20 January 2005 CO2 columnar amount retrieved from thermal infrared spectra observed by IMG/ADEOS
Author Affiliations +
Proceedings Volume 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II; (2005) https://doi.org/10.1117/12.578775
Event: Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2004, Honolulu, Hawai'i, United States
Abstract
Reducing temperature and water vapor estimation errors is indispensable for retrieving a CO2 concentration profile or columnar amount from thermal infrared spectrum data because spectral radiance in the thermal infrared region is much more sensitive to temperature and water vapor concentration changes than it is to CO2 concentration changes. This study presents a data analysis procedure to estimate the CO2 columnar amount from the thermal infrared spectrum. The first step retrieves the temperature vertical profile, water vapor vertical profile, and the surface temperature from spectra in the strong absorption bands of CO2 and H2O. Then the spectral biases that are attributable to temperature and water vapor retrieval errors are reduced by comparing observed and synthesized radiances in the atmospheric window region. The final step estimates the CO2 columnar amount from the corrected spectra of a weak absorption band of CO2 that is located around 940 cm-1. This method was applied to analysis of spectrum data from IMG sensor aboard the ADEOS satellite. Some preliminary results are shown.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ryoichi Imasu and Yoshifumi Ota "CO2 columnar amount retrieved from thermal infrared spectra observed by IMG/ADEOS", Proc. SPIE 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, (20 January 2005); https://doi.org/10.1117/12.578775
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KEYWORDS
Absorption

Infrared radiation

Carbon monoxide

Thermography

Data analysis

Error analysis

Sensors

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