Auto-correcting for atmospheric effects in thermal hyperspectral measurements

J. Timmermans (Corresponding Author), M.F. Buitrago Acevedo, A. Corbin, W. Verhoef

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Abstract

Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H20 (g) and CO2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components.

The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25%. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome.
Original languageEnglish
Pages (from-to)20-28
Number of pages9
JournalInternational Journal of Applied Earth Observation and Geoinformation (JAG)
Volume71
Early online date18 May 2018
DOIs
Publication statusPublished - Sep 2018

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emissivity
Water content
water content
gas
Soils
Gas absorption
atmospheric correction
Radiative transfer
vineyard
Radiometers
Gases
barley
radiometer
vegetation type
simulation
radiative transfer
soil type
land surface
Remote sensing
sensor

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID

Cite this

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title = "Auto-correcting for atmospheric effects in thermal hyperspectral measurements",
abstract = "Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H20 (g) and CO2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components.The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25{\%}. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome.",
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Auto-correcting for atmospheric effects in thermal hyperspectral measurements. / Timmermans, J. (Corresponding Author); Buitrago Acevedo, M.F.; Corbin, A.; Verhoef, W.

In: International Journal of Applied Earth Observation and Geoinformation (JAG), Vol. 71, 09.2018, p. 20-28.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Auto-correcting for atmospheric effects in thermal hyperspectral measurements

AU - Timmermans, J.

AU - Buitrago Acevedo, M.F.

AU - Corbin, A.

AU - Verhoef, W.

PY - 2018/9

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N2 - Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H20 (g) and CO2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components.The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25%. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome.

AB - Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H20 (g) and CO2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components.The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25%. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome.

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