Fang Junyong

Tong Qingxi

Yang Hang

Zhang Lifu

Zhang Xia

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ALGORITHM RESEARCH OF BUILDING MATERIALS 
EMISSIVITY EXTRACTING 
Yang Hang, Zhang Lifu, Fang Junyong, Zhang Xia, Tong Qingxi 
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing 
Applications, Chinese Academy of Sciences, Beijing, 100101 
1. Problems 
   Heat flux estimation and modeling are needed for understanding urban energy 
consumption among which building materials are pivotal. For building materials, temperature 
and emissivity are key parameters in urban remote sensing. Emissivity can be used directly to 
classify and temperature can be used to evaluate the character of building. We know 
estimating temperature and emissivity from radiometric measurements belongs to the problem 
of solving N+1 parameters with N equations. Some approximations or assumptions must be 
taken to reduce the number of unknown parameters and make the equation complete.  
    This paper presents a theoretical study if the algorithm adapted for building materials 
emissivity extracting operating in thermal infrared in order to characterize surfaces from 
ground-based field measurements. The experimental data were obtained with a hyperspectral 
Fourier transform infrared (FTIR) spectroradiometer in emission (BOMEN M304) to 
illustrate the applicability of these algorithms. 
2. Methodology 
For lambertian surface, the thermal radiance received by the sensor can be expressed as 
jatmjatmjjsjjjj LLTBL ,,)1()(                       (1) 
)( sj TB  is the radiance emitted by a black body at the surface temperature ,sT j is the 
surface emissivity, j  is the total atmosphere transmittance.  is the downward radiance 
emitted and diffused by the atmosphere,  is the radiance directed by the atmosphere 
towards the sensor. At ground level,  can be ignored, so the equation (1) can be written as: 
jatmL ,
jatmL ,
jatmL ,
jatmjjsjjjj LTBL ,)1()(                            (2) 
Most of the emissivity extraction methods can be summarized as obtain the optimal surface 
temperature , and then calculated the surface emissivity based on as follows: inverseT
jatminversej
jatmj
j LTB
LL
,
,
)(
                                    (3) 
2.1NEM algorithm 
This method assumes that there is always a band in which the emissivity is maximum for 
all channels for hyperspectral remote sensing. The maximum of calculated temperatures is 
considered to be the surface temperature. This algorithm is simple and reasonable. The 
precision of it depends on the prior. 
2.2 Iterative Spectrally Smooth Temperature-Emissivity Separation (ISSTES) 
This method was first described by Borel. The algorithm’s basis is the assumption that 
the surface emissivity is smoother than the atmospheric downward radiance. Given the 
definition of smoothness for emissivity curves, one can calculated the smoothness of the 
family of emissivity curves correspond to different temperature. The optimal surface 
temperature corresponds to the smoothest emissivity curve, in which the atmospheric 
absorption lines disappear. 
2.3 Maximum-Minimum Difference (MMD) 
MMD is defined as the difference of maximum emissivity and minimum emissivity. The 
scattering plots of MMD and mean emissivity show that MMD is minus relationship with 
mean emissivity. The first step of this algorithm is to obtain the initial value of emissivity 
from radiometric measurements, adjust the initial value of emissivity based on the empirical 
relationship, calculating the temperature with adjusted emissivity and radiometric 
measurements and take its mean value as the surface temperature. This process is repeated 
until the temperature difference of two adjacent times less than the given constant value. 
2.4 Temperature and Emissivity Separation (TES) 
The TES algorithm is the ASTER operational algorithm for temperature and emissivity 
recovery. In fact, this method is an iterative processing: 
(1)The normalized emissivity method (NEM). This step is to prime estimate for object’s 
emissivity. 
(2) Using current emissivity to compute MMD: 
jjjMMD min,max, , j is iterative times.                       (4) 
(3) Using empirical formulation to compute the mean emissivity: 
jj MMDba *1                                            (5) 
(4) Adjusting every band emissivity based on the mean emissivity 
j
j
jiji
1
,1, , i is band number.                                (6) 
(5) Compute the building material surface temperature using the new emissitivity and 
radiance.
(6) , repeat the steps (2)~(5), until to 1jj TNETT jssj ,1 , where  is 
the noise level. 
TNE
3. Central Conclusions 
(1) In order to find derive building materials emissivity accurately, this paper examines 
several existed typical temperature emissivity methods, and assess the precision to hyperspectral 
data. We may conclude that the precision of ISSTES is higher than NEM, so the original NEM 
module has been replaced by ISSTES to acquire the accurate initial value of emissivity in TES. 
(2) We present examples of building materials emissivity extraction using these methods 
mentioned above with ground-based measured hyperspectral data, which were measured by 
BOMEN M304 spectrometer. This data will verify the results of our algorithms. 
(3) Analysis the relationship between the characters of building materials emissivity and heat 
transfer characteristic. This will be useful to evaluate the heat preservation of different building 
materials. 
4. References 
[1] Zhang Jian-xian, Zhou Yong-jun, and Xu Lei. High-emissivity Coating and Its Application. 
Infrared Technology. 2007, 29(8): 491-494. 
[2] J.A. Voogt and T.R. Oke. Thermal Remote Sensing of Urban Climates. Remote Sensing of 
Environment. 2003,86:370-384. 
[3] Cesar Coll, Vicente Caselles, and Eva Rubio, et. al. Temperature and Emissivity 
Separation from Calibrated Data of the Digital Airborne imaging Spectrometer. Remote 
Sensing of Environment. 2003,76:250-259. 
[4] Cesar Coll, Vicente Caselles, and Eva Rubio, et. al. Validation of Temperature-emissivity 
Separation and Split-window Methods from TIMS Data and Ground Measurements. Remote 
Sensing of Environment. 2003,85:232-242. 
[5] V. Payan and A. Royer. Analysis of Temperature Emissivity Separation (TES) algorithm 
applicability and sensitivity. International Journal of Remote Sensing. 2004, 25(1):pp:15-37. 
[6] Gillespie A. R. Rokugawa S. Hook S. et al. A temperatureand emissivity separation 
algorithm for Advanced Spaceborne ThermalEmission and Reflection Radiometer ASTER
image  J . and Reflection Radiometer ASTER image  J . IEEE Transaction on 
Geoscience and Remote Sensing 1998 36 1113 -1126. 
[7] Donald E. Sabol, Jr., Alan R. Gillespie, Elsa Abbott et. al. Field Validation of the ASTER 
Temperature-Emissivity Separation algorithm. Remote Sensing of Environment. 
2009,112:2328-2344. 


ALGORITHM RESEARCH OF BUILDING MATERIALS EMISSIVITY EXTRACTING

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ALGORITHM RESEARCH OF BUILDING MATERIALS EMISSIVITY EXTRACTING

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