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.
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Nothing for ENVI, but maybe of interest for GRASS GIS users: github.com/NikosAlexandris/i.landsat8.swlst– Nikos AlexandrisCommented Jun 9, 2015 at 7:18
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thanks for your camplete answer. can you tell me how i can find radiometric calibration in envi4.8?– user61770Commented Nov 4, 2015 at 6:09
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Calculation of ground surface temperature by method split window in envi– arastou zareiCommented Nov 20, 2018 at 0:47
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3 Answers
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:
Add: 0.1
Multiply by: 0.0003342 (3.3420E-04)
In ENVI you can apply this correction using 'band math':
float(b10)*0.0003342+0.1
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)
answered Mar 5, 2015 at 8:50
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are you sure that the last step direct to LST and not Brightness temperature ? In the paper of Rajeshwari, A. (2014). "Estimation of land surface temperature of Dindigul district using Landsat 8 data." they use this formula eq (2) but to have the brightness temperature which is then use in eq (1) for deriving the LST.– NanBlancCommented Feb 15, 2022 at 18:35
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1@NanBlanc - the method outlined above is the 'standard', easy-to-use approach, which simplifies the process a lot. Sometimes, the simple approach is referred to as 'land surface temperature', but when considering more elements, it is also called 'brightness temperature'. There are other, more complex methods, such as the split-window + NDVI approach, outlined in Rajeshwari, 2014. This method also simplifies things and only really works in vegetated areas, where NDVI can be assumed to represent most of the occurring variance. Applying this method in urban areas would provide incorrect results. Commented Feb 16, 2022 at 9:03
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1If a more complex method is needed, then I'd recommend something along the lines of Malakar et al., 2018 - ieeexplore.ieee.org/document/8361068 . It is a lot more work, but it actually accounts for more than just vegetation. Commented Feb 16, 2022 at 9:05
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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.
