MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS

Berk, Alexander; Bernstein, L. S.; Anderson, Gail P.; Acharya, Prabhat K.; Robertson, David; Chetwynd, James H.; Adler‐Golden, Steven M.

doi:10.1016/s0034-4257(98)00045-5

Cited by 631 publications

(303 citation statements)

References 23 publications

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“…The effect of assuming that the plume is planeparallel is inconsequential since the retrieval determines the SCD, and hence the actual geometry of the plume is irrelevant. Other aspects of the radiative transfer include use of the MODTRAN-4 code, which has undergone detailed scrutiny and intercomparison (Berk et al, 1998). It is difficult to make a precise estimate of the likely impact of errors in the radiative transfer modelling on the retrieval, but RT models suggest errors of 0.2-0.5 K are possible (Strow et al, 2003).…”

Section: Type 2 Errorsmentioning

confidence: 99%

Retrieval of sulfur dioxide from a ground-based thermal infrared imaging camera

Prata

Bernardo

2014

Atmos. Meas. Tech.

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Abstract. Recent advances in uncooled detector technology now offer the possibility of using relatively inexpensive thermal (7 to 14 µm) imaging devices as tools for studying and quantifying the behaviour of hazardous gases and particulates in atmospheric plumes. An experimental fast-sampling (60 Hz) ground-based uncooled thermal imager (Cyclops), operating with four spectral channels at central wavelengths of 8.6, 10, 11 and 12 µm and one broadband channel (7-14 µm) has been tested at several volcanoes and at an industrial site, where SO 2 was a major constituent of the plumes. This paper presents new algorithms, which include atmospheric corrections to the data and better calibrations to show that SO 2 slant column density can be reliably detected and quantified. Our results indicate that it is relatively easy to identify and discriminate SO 2 in plumes, but more challenging to quantify the column densities. A full description of the retrieval algorithms, illustrative results and a detailed error analysis are provided. The noise-equivalent temperature difference (NE T ) of the spectral channels, a fundamental measure of the quality of the measurements, lies between 0.4 and 0.8 K, resulting in slant column density errors of 20 %. Frame averaging and improved NE T 's can reduce this error to less than 10 %, making a stand-off, day or night operation of an instrument of this type very practical for both monitoring industrial SO 2 emissions and for SO 2 column densities and emission measurements at active volcanoes. The imaging camera system may also be used to study thermal radiation from meteorological clouds and the atmosphere.

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Section: Type 2 Errorsmentioning

confidence: 99%

Retrieval of sulfur dioxide from a ground-based thermal infrared imaging camera

Prata

Bernardo

2014

Atmos. Meas. Tech.

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“…3.1 Simulation of TOA brightness temperature diff erences Brightness temperatures (BTs) to be observed by MODIS at various satellite zenith angles (0-60 ) were simulated using radiative transfer (RT) code MODTRAN (Berk et al, 1998) for various surface types of hoar, snow, and ice. The effects of the different surface types on BTs were examined by exchanging ground reflectances (i.e., 1-emissivity) which were used as a boundary condition in this RT simulation.…”

Section: Methodsmentioning

confidence: 99%

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

Hori

Aoki²,

Tanikawa

et al. 2014

Bulletin of Glacier Research

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The potential of the thermal infrared (TIR) remote sensing for discriminating surface snow types was examined by analyzing TIR radiances acquired from space over the Greenland ice sheet. The brightness temperature difference (BTD) between TIR wavelengths of 11 and 12 m was found to increase in accordance with in situ observed evolutions of surface snow type. Spatial and temporal distributions of BTD over the entire ice sheet indicated that BTD has a sensitivity of about 1.2 K for variations of the possible snow types. The observed behaviors of BTD were coincident with those predicted by a radiative transfer calculation using previous in situ measured snow emissivities, although some biases on the order of 0.1-0.3 K remain. The dependence of BTD on the surface snow type was also consistent with the behaviors of snow reflectance at the shortwave infrared (SWIR) wavelength 1.6 m, which is a measure of snow grain size, except for the case of melting wet snow. The inconsistency in the wet snow case was considered to be due to the different optical responses of the TIR and SWIR signals to wet snow, which suggested the possibility of using TIR signals to discriminate wet/dry conditions of snow cover in an old stage. As a result, it is determined that TIR remote sensing has potential not only as an approach supplementary to the SWIR method for assessing surface snow types in daytime but also as the only method for simultaneous retrieval of snow type and surface temperature in nighttime.

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“…The MODTRAN radiative transfer code [Berk et [Berk et al, 1998]. This investigation is dependent on the good relative accuracy of MODTRAN for spectra modeled with different amounts of water vapor.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric water vapor sensitivity and compensation requirement for Earth‐looking imaging spectrometers in the solar‐reflected spectrum

Green¹

2001

J. Geophys. Res.

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Abstract. An increasing number of Earth-looking imaging spectrometers are being developed to derive properties of the surface from measured upwelling spectral radiance that has passed through the atmosphere. However, the measurements are influenced by the amount of atmospheric water vapor. Water vapor is present in every spectrum and is highly variable through space and time. This research assesses the sensitivity to the accurate knowledge of atmospheric water vapor of imaging spectrometers with surface measurement objectives. This sensitivity is explored for a range of imaging spectrometer spectral resolutions from 5 to 20 nm as well as for a range of atmospheric water vapor from normal to dry conditions. The effect of atmospheric water vapor errors of 50, 25, 10, 5, and 1% is analyzed. In all cases the derived surface reflectance is shown to be strongly sensitive to the accurate knowledge of water vapor in the imaging spectrometer measurement. This result leads to a general requirement of 1% or better knowledge of atmospheric water vapor in order to derive surface reflectance that is largely free of water-vapor-induced errors, distortions, and spectral artifacts.

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MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS

Cited by 631 publications

References 23 publications

Retrieval of sulfur dioxide from a ground-based thermal infrared imaging camera

Retrieval of sulfur dioxide from a ground-based thermal infrared imaging camera

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

Atmospheric water vapor sensitivity and compensation requirement for Earth‐looking imaging spectrometers in the solar‐reflected spectrum

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