Extending surface temperature and emissivity retrieval to the mid-infrared (3-5 @mm) using the Multispectral Thermal Imager (MTI) [An article from: Remote Sensing of Environment] Buy on Amazon

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Extending surface temperature and emissivity retrieval to the mid-infrared (3-5 @mm) using the Multispectral Thermal Imager (MTI) [An article from: Remote Sensing of Environment]

Book Details

PublisherElsevier
ISBN / ASINB000RR6RNA
ISBN-13978B000RR6RN4
MarketplaceFrance  🇫🇷

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This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Surface-temperature (T) and emissivity (@e) estimation from remotely sensed mid-infrared (MIR: 3-5 @mm) data requires modifications to existing long-wave infrared (LWIR: 8-12 @mm) T/@e separation algorithms because of the significantly different characteristics of Planck's function between the MIR and LWIR wavelength regions and the strong effects of reflected solar irradiance in the MIR. A modified version of the normalized emissivity method (NEM), utilizing independently scaled maximum emissivities in each channel, was applied to thermal data acquired by the Multispectral Thermal Imager (MTI) with two MIR channels, J (3.81 @mm) and K (4.97 @mm), and three LWIR channels, L (8.26 @mm), M (8.65 @mm), and N (10.51 @mm). Atmosphere-free simulations of T and @e retrieval over a wide variety of terrestrial surfaces yielded T values within +/- 0.75 K of 'true T' in the range of 270-330 K and @e values within +/-0.011 of true @e in channels L, M and N, and +/-0.019 and +/-0.023 in channels K and J, respectively. The algorithm was tested successfully using MTI data over the Mauna Loa caldera in Hawaii. Unconstrained effects of shading and unresolved shadows in channel J day time data, and the strong atmospheric effects in channel K limit the application of the algorithm, in its present form, to night-time data.
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