|
Emissivity Determination for Land Surface Temperature estimation of Iran using AVHRR Thermal Infrared Data
4. Modeling Various climate conditions are present at the same time in one season in different places in Iran. Therefore, determination of exact coefficient for LST algorithm for the whole country requires more ground observations in different seasons. These observations were collected. Since deserts in Iran have almost similar climate conditions, they have been selected to deal with separately. Boundaries of deserts are shown in Figure 4.  Figure 4: Boundaries of Iran’s deserts As mentioned, the brightness temperature measured by a satellite sensor differs from the ground surface temperature because of effects of surface emissivity and the attenuation due to the intervening atmosphere. In order to account surface emissivity effect in LST model, emissivity value has been applied on brightness temperature using Stephan-Boltzmann law (Equation 4). Trad = e1/4 Tkin (4) where Trad is kinetic temperature, Tkin is radiation temperature and e is emissivity. Split window algorithm has been applied to reduce atmospheric effect (Equation5). The angular characteristics of surface emissivity has not been considered at the determination of split-window equation, but taking into account zenith angle, the effect of the longer atmospheric path length has been minimized. TS = a T4+b (T4-T5) +c (T4-T5) (Secq-1) + d (5) where T4 and T5 are brightness temperatures in channel 4 and 5 respectively, and q is satellite zenith angle. In order to determine model coefficients, 25 meteorological observations in desert regions were selected from which 7 observations were omitted based on statistical analysis. The correlation coefficients of meteorological data and satellite data have been shown in Table 2. Table2. Correlation of satellite data and meteorological data
The model coefficient values (i.e. a, b, c and d) have been computed using a least-square linear regression for deserts of Iran (Table 3). The RMSE for the coefficients is 1.13. Table3. Coefficients of LST algorithm
Plots for brightness temperatures in channel 4 and 5, meteorological observations, and calculated temperature (LST) using the model constructed for desert regions have been shown in figures 5. Also a comparison made on the calculated LST at each point with relevant observation have been shown in Figure 6.  Figure 5. Plot of Brightness Temperature, Meteorological data and LST  Figure 6. Comparison of Observations and LST  Figure 7. Plot of Brightness Temperature, Ground data and LST  Figure 8. Comparison of ground data and LST In order to check accuracy of computed coefficients, 8 ground observations data have been used. The correlation coefficient for ground observations and satellite data is 0.92 and corresponding RMSE obtained using desert model is 1.1398. Plots for ground observations, corresponding Tb4 and Tb5, and LST using the desert model have been shown in figures 7. Also a comparison made on the LST at each point with relevant observation have been shown in Figure 8. As mentioned, Iran is a big country with high diversity in land cover. The great difference in land cover and type and accordingly presence of different emissivity values makes the LST algorithm or model development more complicated. Therefore, determination of exact coefficients for LST algorithm for the whole country requires more ground observations and analysis, hence, the model coefficients is at final calibration stage in this study. |