LAB 1: Temperature extraction from thermal remote sensing data

Goal and Background
The goal of this lab is to understand and utilize the skills for extracting land surface temperatures from thermal bands from satellite images, and additionally account for these variations in land surface temperature over different areas. This lab will allow us to visually identify variations in relative land surface temperature. Model building will be used to quantitatively estimate surface temperatures from thermal bands, then more complex models will be utilized to accomplish the goal in one model rather than using multiple models or steps.

 Methods
Part 1: Visual identification of relative variations in land surface temperature
To begin the first part of this lab,  Landsat ETM+ images from band 61 and 62 are utilized. In this portion of the lab the tonal quality of each image is analyzed to determine temperature differences. Then the spectral range, and spatial resolution were determined.this portion of the lab helped in the process of understanding the differences between thermal images, and reflective images.

Part 2: 
Setion 1: Calculation of land surface temperature from ETM+ image. 
In this section of the lab, 2 models were utilized. The first model converted a thermal band image into an image where surface temperature can be analyzed, this is done through creating a model, and using the equation  Grescale * DN + Brescale. Grescale is found by finding Lmax Lmin, QCALmax, and QCALmin in the satellites Metadata. Then the equation (Lmax-Lmin)/(Qcalmax-Qcalmin)= grescale is used. In the Case of the Landsat ETM= image band 62, the equation was as follows for grescale: (12.65-3.2)/(255-1)=.0372. this number is then put back into the full equation to calculate the surface temperature. this equation that goes into the function of the model is: 0.0372*eau_claire2000_b62+3.2 the 3.2 comes from the Lmin wich is also the Brescale. After all this is entered into the model, an output is produced, this one is called n3_eau_claire2000_b62rad. 


Section 2. Conversion of at-satellite radiance to blackbody surface temperature
Next in section 2 of part 2, the at-satellite radiance is converted to blackbody surface temperature. this is done through the use of another model, where the surface temperature output is used to create a surface temperature that is different from the kinetic (true) temperature. This is done using the equation in Figure 1. This equation is the function that converts at-satellite radiance to blackbody surface temperature. 
Figure 1: This equation converts at-satellite radiance to blackbody surface temperature.

Part 3: Calculation of land surface temperature from TM image. 
In this portion of the lab, instead of using Landsat ETM+, Landsat TM is utilized and the same process is done as in part 2, except the model is crreated to perform both actions at once rather than in seperate steps.  

Part 4: Calculation of land surface temperature from Landsat 8 image  
This final portion of the lab utilizes Landsat 8 imagery, and a similar model and function as used in part 3. This model once again incorporates all the steps in order to determine surface features through thermal bands. The image in this part is from 2014, and a area of interest file is utilized to create a subset image, where the same processes as above are performed.

Results
Part 1: Visual identification of relative variations in land surface temperature
This portion of the lab gave the understanding of what thermal bands consist of. The tonal differences between band 61 and 62 show differences in temperature. Then when we look at differences between thermal and reflective bands, it is understood that thermal waves are longer than reflective waves therefore a greater spectral resolution is necessary to distinguish the difference in temperatures across the land surface.
Part 2: 
Section 1: Calculation of land surface temperature from ETM+ image. 
The model that was ran in this part of the lab is shown in Figure 2. The output image was then brought into ArcMap where the surface temp values were assigned to colors to better represent the different surface temperatures.
Figure 2: This Image shows the model and function that was used to produce the surface temperature output from Landsat ETM+.
Section 2: Conversion of at-satellite radiance to blackbody surface temperature
When this function is performed, the new blackbody surface temperature is produced. the model looks like Figure 3. Figure 4 shows what the image looks like after being brought into ArcMap, and applying different colors to surface temperatures.
Figure 3: This image shows the model used to convert to blackbody surface temperature.

Figure 4: This shows the temperature image for part 2 where darker red colors are warm temperatures, and green values are low temperatures.

 Part 3: Calculation of land surface temperature from TM image.
The model that is created to perform these actions at once looks like Figure 5. this model performs both actions at once producing a surface temperature image that has been converted in Arcmap to look like Figure 6. 
Figure 5: This model shows both steps at once.
Figure 6: This image is produced from the model in figure 5, but the colors have been changed in ArcMap so dark red represents warmer features, and lighter red cooler features.

Part 4: Calculation of land surface temperature from Landsat 8 image  
In this section, the Landsat 8 image is used the model is shown in figure 7, and the map produced from this model is shown in figure 8.this map shows the temperature of land surface features in Kelvin.
Figure 7: this is the model used in producing the map in figure 8.
Figure 8: This map shows the surface temperature of Eau Claire and Chippewa County on May 23,2014 at 9:48am local time.


Sources
Landsat satellite image is from Earth Resources Observation and Science Center, United States Geological Survey. Area of interest (AOI) file is derived from ESRI counties vector features.  

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