I am doing research about land surface temperature using Landsat 8 image. However, I have a problem with them. I have read document from Landsat.org, but i can not do it in Envi. Could you help me step-by-step to calculate land surface temperature using landsat 8 TIRS in Envi 5.1.
Step 1: Convert from digital numbers (DN) to radiance
This is done by applying the multiplier and addition numbers as found in the metadata (.MTL) file. For the thermal bands (B10 and B11), the values are usually, but you should check the file:
Multiply by: 0.0003342 (3.3420E-04)
In ENVI you can apply this correction using 'band math':
This gives you the radiance value.
Step 2: Convert from radiance to kelvin
The formula needed here is
K2 / ln(K1/TOA_r + 1)
Again, the important values can be found in the metadata file. Usually, the K1 and K2 values are as follows:
K1_CONSTANT_BAND_10 = 774.89
K1_CONSTANT_BAND_11 = 480.89
K2_CONSTANT_BAND_10 = 1321.08
K2_CONSTANT_BAND_11 = 1201.14
In ENVI band math the formula becomes:
1321.08 / alog(774.89/B1+1)
Where alog is the ENVI band math version of the natural log.
This could be combined into one step - for example, band 11 becomes:
1201.14 / alog(480.89/(float(b11)*0.0003342+0.1)+1)
The best way i can recommend is to use "Radiometric Calibration" tool of ENVI. In this, no manual calculation is required.
Step 1: Open the MTL file from ENVI (File-> Open) . When Landsat 8 images are downloaded, they provide with .MTL text file (eg. LC81920252013135LGN01_MTL)
Step 2: Search in Toolbox for "Radiometric Calibration".As you select the tool,it will provide you with three band option: Multispectral, Thermal and Panchromatic . Select the thermal as shown in figure below. Click on "OK".
Step 3: Select the option of "Brightness Temperature" from drop-down menu, as shown in figure below. Save the output data to your computer. (If image to be atmospherically corrected for fog, click FLAASH for default correction)
The paper utilized Landsat 5 TM and Landsat 8 OLI for analyzing land use/land cover change and its impact on land surface temperature in Sundarban Biosphere Reserve, India. Split window algorithm and spectral radiance model were used for determining land surface temperature from Landsat 8 OLI and Landsat 5 TM, respectively. The land use land cover change analysis revealed phenomenal increase in the waterlogged areas followed by settlement and paddy and a decrease in open forest followed by deposition and water body. The distribution of average change in land surface temperature shows that water recorded highest increase in temperature followed by deposition, open forest and settlement. Overlay of the transect profiles drawn on land use/land cover change map over land surface temperature map revealed that the land surface temperature has increased in those areas which were transformed from open forest to paddy, open forest to settlement, paddy to settlement and deposition to settlement. The study demonstrated that increase in non-evaporating surfaces and decrease in vegetation have increased the surface temperature and modified the temperature of the study area.