This post is about the estimation land surface temperature using Landsat Satellite and ASTER Satellite images. In this tutorial we are going to use a land cover classification for the definition of surface emissivity, which is required for the calculation of the land surface temperature. It is assumed that one has the basic knowledge of SCP and Basic Tutorials.
Our study area will be Paris (France), an area covered by urban surfaces, vegetation and agricultural fields.
Before downloading data, please watch the following video that illustrates the study area and provides very useful information about thermal infrared images, and their application (footage courtesy of European Space Agency/ESA). Also, a brief description of the area that we are going to classify is available here .
The thermal infrared band is particularly useful for assessing the temperature difference between the city and the surrounding rural areas, and studying the urban heat island phenomenon. We are going to use Landsat and ASTER images for the estimation of land surface temperature. For more information about the conversion of raster bands please read Conversion to At-Satellite Brightness Temperature. Following the video of this tutorial.
1. Data Download and Conversion
We are going to download the Landsat 8 image acquired in 2015 (image ID =LC81990262015270LGN00
, data available from the U.S. Geological Survey).Start a new QGIS project. Open the tab Download images clicking the button

In Login https://ers.cr.usgs.gov/ you should enter the user name and password for accessing data (free registration at USGS EROS is required) in User and Password. However, in this case login should not be required because this Landsat 8 image is available directly from the Amazon Web Services (AWS) .
In Search area enter:
- UL X (Lon): 2
- UL Y (Lat): 49
- LR X (Lon): 2.5
- LR Y (Lat): 48.8
TIP : In general it is possible to define the area coordinates clicking the button
and drawing a rectangle in the map.
L8 OLI/TIRS
from the list Satellites and set the acquisition date:- Date from: 2015-09-27
- to: 2015-09-27

Image list
.In the result table, click the item
LC81990262015270LGN00
in the field ImageID, and click the button 
Image preview
- 2 = Blue
- 3 = Green
- 4 = Red
- 5 = Near-Infrared
- 6 = Short Wavelength Infrared 1
- 7 = Short Wavelength Infrared 2
- 10 = Thermal Infrared (TIRS) 1
The checkbox



In order to start the download and conversion process, open the tab Landsat download, click the button

Desktop
). After a few minutes, converted bands are loaded and displayed (file name starts with RT_
).2. Clip to Study Area
We are going to clip the Landsat images to our study area.Open the tab Preprocessing clicking the button



- UL X: 402705
- UL Y: 5461065
- LR X: 480824
- LR Y: 5381535
Now click the button

Desktop
). Clipped bands have the prefix clip_
and will be automatically loaded and displayed. We can remove the bands whose names start with RT_
from QGIS layers.
Clipped bands
3. Land Cover Classification
Now we need to classify land cover, which will be used later for the creation of the emissivity raster. For detailed instructions about the classification process please see Tutorial 2: Land Cover Classification of Sentinel-2 Images.We are going to use the following Macroclass IDs (see Classes and Macroclasses).
Macroclasses
Macroclass name | Macroclass ID |
---|---|
Water | 1 |
Built-up | 2 |
Vegetation | 3 |
Bare soil | 4 |
Open the tab Band set clicking the button

In the SCP dock click the button

4-3-2
to display a false color composite corresponding to the bands: Near-Infrared, Red, and Green (see Color Composite).
Color composite
After the creation of several ROIs for each land cover class, we can perform the classification of the whole image (see Tutorial 2: Land Cover Classification of Sentinel-2 Images). After setting the colors of MC ID (in the tab Macroclasses of the SCP dock), in the tab Classification algorithm check the option

Then, open the tab Classification output, click the button

Land cover classification
4. Reclassification of Land Cover Classification to Emissivity Values
Now we are going to reclassify the classification raster using the land surface emissivity values. The emissivity (e) values for the land cover classes are provided in the following table (values used in this tutorial are only indicative, because emissivity of every material should be obtained from field survey):Emissivity values
Land surface | Emissivity e |
---|---|
Water | 0.98 |
Built-up | 0.94 |
Vegetation | 0.98 |
Bare soil | 0.93 |
Open the tab Postprocessing clicking the button


Click the button



emissivity.tif
).
Reclassification
This is the emissivity raster, where each pixel has the emissivity value that we have defined for the respective land cover class.
Emissivity raster
5. Conversion from At-Satellite Temperature to Land Surface Temperature
Now we are ready to convert the At-Satellite Brightness Temperature to Land Surface Temperature, using the following equation (see Estimation of Land Surface Temperature):- = wavelength of emitted radiance
- m K = 14388 µm K
- = Planck’s constant = J s
- = Boltzmann constant = J/K
- = velocity of light = m/s
Center wavelength of Landsat bands
Satellite | Band | |
---|---|---|
Landsat 4, 5, and 7 | 6 | 11.45 |
Landsat 8 | 10 | 10.8 |
Landsat 8 | 11 | 12 |
Open the tab Band calc clicking the button


"clip_RT_LC81990262015270LGN00_B10.tif" / ( 1 + ( 10.8 * "clip_RT_LC81990262015270LGN00_B10.tif" / 14388 ) * ln("emissivity.tif") )
Click the button

surface_temperature.tif
). After the calculation, the Land Surface Temperature (in kelvin) will be loaded, and we can change the layer style. In addition, in the tab Band calc we can calculate the temperature in Celsius with the expression:"surface_temperature.tif" - 273.15
We can notice that the urban area and uncultivated land have the highest temperatures, while vegetation has the lowest temperature. The aim of this tutorial is to describe a methodology for the estimation of surface temperature using open source programs and free images. It is worth highlighting that in order to achieve more accurate results, one should perform field survey for improving the land cover classification and the estimation of surface emissivities.
In addition to Landsat, we are going to use an ASTER image and use the same methodology for the estimation of Land Surface Temperature.
6. Data Download and Conversion of ASTER Image
Open the tab Download images and select the tab ASTER download.In Login https://urs.earthdata.nasa.gov enter the user name and password required for accessing data (free registration at EOSDIS Earthdata is required) in User and Password. The ASTER L1T data products are retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota,https://lpdaac.usgs.gov/data_access/data_pool.
In Search area enter:
- UL X (Lon): 2
- UL Y (Lat): 49
- LR X (Lon): 2.5
- LR Y (Lat): 48.8
- Date from: 2000-08-24
- to: 2000-08-24

Image list
.In the result table, click the item
AST_L1T_00308242000111313_20150411071856_3805
in the field ImageID, and click the button 
As we did for Landsat, we are going to apply the DOS1 Correction. The checkbox

Open the tab ASTER, check


In order to start the download and conversion process, open the tab ASTER download, click the button

Desktop
). After a few minutes, converted bands are loaded and displayed (file name starts with RT_
).7. Clip to Study Area of ASTER image
We are going to clip the ASTER images to our study area, because bands are not aligned at the border.Open the tab Preprocessing clicking the button


Now click the button

Desktop
). Clipped bands have the prefix clip_
and will be automatically loaded and displayed. We can remove the bands whose names start with RT_
from QGIS layers.8. Land Cover Classification of ASTER Image
Using the same Macroclass IDs used for Landsat, we are going to classify the ASTER image.Open the tab Band set clicking the button


In the SCP dock click the button

training_ASTER
). Clear the table ROI Signature list highlighting all the spectral signatures created previously for Landsat and clicking the button 
In the list RGB= of Working toolbar select
3-2-1
to display a false color composite corresponding to the bands: Near-Infrared, Red, and Green (see Color Composite).After the creation of several ROIs for each land cover class, we can perform the classification of the whole image. After setting the colors of MC ID (in the tab Macroclasses of the SCP dock), in the tab Classification algorithm check the option


classification_aster.tif
).9. Reclassification of Land Cover Classification to Emissivity Values of ASTER Image
Now we are going to reclassify the classification raster using the same land surface emissivity values used for Landsat.Open the tab Postprocessing clicking the button


Click the button



emissivity_aster.tif
).
ASTER reclassification
10. Conversion from At Satellite Temperature to Land Surface Temperature of ASTER Image
We can convert the At-Satellite Brightness Temperature to Land Surface Temperature, using the same equation used for Landsat (see Estimation of Land Surface Temperature).The values of for ASTER bands are listed in the following table.
Center wavelength of ASTER bands
Satellite | Band | |
---|---|---|
ASTER | 10 | 8.3 |
ASTER | 11 | 8.65 |
ASTER | 12 | 9.1 |
ASTER | 13 | 10.6 |
ASTER | 14 | 11.3 |
We are going to use ASTER band 13 that has a value very similar to the Landsat band 10.
Open the tab Band calc clicking the button


"clip_RT_AST_L1T_00308242000111313_20150411071856_3805_13.tif" / ( 1 + ( 10.6 * "clip_RT_AST_L1T_00308242000111313_20150411071856_3805_13.tif" / 14388 ) * ln("emissivity_aster.tif") )
Click the button

surface_temperature_aster.tif
). After the calculation, the Land Surface Temperature (in kelvin) will be loaded, and we can change the layer style. In addition, in the tab Band calc we can calculate the temperature in Celsius with the expression:"surface_temperature_aster.tif" - 273.15
The ASTER image shows temperature values higher than the Landsat image. For instance, we could perform the difference between the two surface temperature rasters (Landsat and ASTER) to assess the variation of temperature. However, we should notice that the two images were acquired in different months (Landsat on 27-09-2015 and ASTER on 24-08-2000).
The large availability of Landsat and ASTER images for the past decades allows for the reliable monitoring of land cover and surface temperature. Nevertheless, cloud cover can limit the number of images that can be effectively used.
This tutorial illustrated a methodology of temperature estimation using these satellite images and open source programs. One should always consider that the estimation accuracy depends on several factors, such as the thematic and spatial accuracy of land cover classifications and the reliability of the emissivity values. Estimation errors can be of 1 K or even more. Other methods have been developed which can provide more accurate results, and the reader can continue the research.
For any comment or question, join the Facebook group and the Google+ Community about the Semi-Automatic Classification Plugin.