Longwave infrared (LWIR) spectroscopy is useful
for detecting and identifying hazardous clouds by passive remote
sensing technology. Gaseous constituents are usually assumed
to be thin plumes in a three-layer model, from which the
spectral signatures are linearly superimposed on the brightness
temperature spectrum. However, the thin-plume model performs
poorly in cases of thick clouds. A modification to this method
is made using synthetic references as target spectra, which
allow linear models to be used for thick clouds. The prior
background, which is generally unknown in most applications,
is reconstructed through a regression method using predefined
references. However, large residuals caused by fitting errors may
distort the extracted spectral signatures and identification results
if the predefined references are not consistent with the real
spectral shapes. A group of references are generated to represent
the possible spectral shapes, and the least absolute shrinkage and
selection operator (LASSO) method is used to select the most
appropriate reference for spectral fitting. Small residuals and
adaptive identification are achieved by automatically selecting the
reference spectrum. Two experiments are performed to verify the
algorithm proposed in this article. Ethylene is adaptively detected
during an indoor release process, and the spectral shape varies
with the amount released. In addition, ammonia is measured
under different humidity conditions, and the background is
adaptively removed using the LASSO method. Based on this
research, LWIR remote sensing technology can be applied in
various target-detection scenarios, and adaptive identification is
achieved to promote hazardous cloud detection.