Land-use and land-cover (LULC) data are often
employed for simple correlation analyses between LULC types
and their thermal signatures in the studies of land surface
temperature (LST) using remote sensing. This tends to slow
down the development of remote sensing of land surface
temperature. Hence, there is need for methodological shift to
quantitative surface descriptors. Development of quantitative
surface descriptors could improve our capabilities for
modeling urban thermal landscapes and advance urban
climate research. This study therefore adopted an analytical
procedure based on a spectral derivation model for
characterizing and quantifying the urban landscape in Jimeta,
Nigeria. A Landsat Enhanced Thematic Mapper Plus (ETM+)
image of the study area, acquired on 16 November 2008, was
spectrally modeled into three fraction endmembers namely,
green vegetation, soil, and impervious surface. A hybrid
classification procedure was developed to classify the fraction
images into six land-use and land-cover classes. Next, pixelbased
LST measurements were related to urban surface
biophysical descriptors derived from spectral mixture analysis
(SMA). Correlation analyses were conducted to investigate
land-cover based relationships between LST and impervious
surface and green vegetation fractions for an analysis of the
causes of LST variations. Results indicate that fraction images
derived from SMA were effective for quantifying the urban
morphology and for providing reliable measurements of
biophysical variables such as vegetation abundance, soil and
impervious surface. An examination of LST variations within
the city and their relations with the composition of LULC
types, biophysical descriptors, and other relevant spatial data
shows that LST possessed a weak relation with the LULC
compositions than with other variables (including urban
biophysical descriptors, remote sensing biophysical variables,
GIS-based impervious surface variables, and population
density).