An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
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Date
2001-02
Authors
Liang, Shunlin
Advisor
Citation
Liang, S., (2001), An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery, IEEE Transactions on Geoscience and Remote Sensing, 39: 264-274.
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Abstract
Land surface temperature (LST) and emissivity are
important components of land surface modeling and applications.
The only practical means of obtaining LST at spatial and temporal
resolutions appropriate for most modeling applications is through
remote sensing. While the popular split-window method has
been widely used to estimate LST, it requires known emissivity
values. Multispectral thermal infrared imagery provides us with
an excellent opportunity to estimate both LST and emissivity
simultaneously, but the difficulty is that a single multispectral
thermal measurement with bands presents equations in
+ 1 unknowns ( spectral emissivities and LST). In this study,
we developed a general algorithm that can separate land surface
emissivity and LST from any multispectral thermal imagery, such
as moderate-resolution imaging spectroradiometer (MODIS) and
advanced spaceborne thermal emission and reflection radiometer
(ASTER). The central idea was to establish empirical constraints,
and regularization methods were used to estimate both emissivity
and LST through an optimization algorithm. It allows us to
incorporate any prior knowledge in a formal way. The numerical
experiments showed that this algorithm is very effective (more
than 43.4% inversion results differed from the actual LST within
0.5 , 70.2% within 1 and 84% within 1.5 ), although improvements
are still needed.