Lab 2: Acquiring Surface Temperature from Thermal Images

Background and Goal
The main goal of this lab is to learn and understand how to acquire surface temperature information from remotely sensed images in a thermal band. This is necessary because thermal sensors only record radiant heat, and not kinetic heat. Kinetic heat is the true temperature of an object, while radiant heat is merely the heat that radiates from the object. This heat is measured in watts, while kinetic heat is measured in the familiar degrees of Fahrenheit, Celsius, or even Kelvin. In remote sensing software, a thermal image displays the radiant heat of scanned objects, and it is through the following process that these images are converted so that they display kinetic heat, or surface temperature. The final goal of this lab is to create a surface temperature map of the Eau Claire and Chippewa Counties.

Methods

The transfer of thermal data from radiant heat to kinetic heat involves three important equations. The first equation (Figure 1) converts the digital numbers (DN) of the image to at-satellite radiance. This equation results in an image that reveals the spectral radiance of the image (L_λ). Grescale is the rescaled gain of the image, and Brescale is the rescaled bias. Grescale is calculated by using the second equation (Figure 2), where LMAX is the spectral at-sensor radiance that is scaled to Q_calmax and LMIN is the spectral at-sensor radiance that is scaled to Q_calmin. QCALMIN is the minimum quantized calibrated pixel value that corresponds to LMIN and QCALMAX is the maximum quantized calibrated pixel value that corresponds to LMAX. All of this information is found in the image’s metadata. Brescale is equivalent to LMIN. The final equation (Figure 3) converts at-satellite radiance to a surface temperature in Kelvin (T_B). L_λ is the radiance image that was created in equation one, while K₂ and K₁ are calibration constants that are specific to each different satellite.

Figure 1: The first equation in the process, revealing the spectral radiance of the image.

Figure 2: The second equation in the process, used to acquire the grescale (gain) of the image.

Figure 3: The third equation in the process, resulting in the final surface temperature image.

For this exercise, we used data from the Landsat 8 satellite, using an information from band 10 (Thermal Infrared). The raw thermal image can be seen in Figure 4. First, it was required to examine the metadata in order to find the LMAX, LMIN, QCALMAX, and QCALMIN values. For Landsat 8, the calibration constants are in the metadata as well. The values were then put into an excel table in order to complete equation two and get the grescale (Figure 5). From here, the model maker in ERDAS Imagine was used to process the rest of the equations. The model that was used can be seen in Figure 6. The model consists of three raster objects and two functions. The first raster object is the thermal image of the at-satellite radiance values (essentially DN from equation one). This raster is used in the first function (Figure 7) to create the second raster (essentially L_λ from equations one and three). However this is merely a temporary file, only being used to complete the final equation, in the second function of the model (Figure 8). This function creates an output file (the final raster), which is our surface temperature image.

Figure 4: The raw thermal infrared image.

Figure 5: The execution of equation two to acquire the grescale value. The equation
can be seen in the function bar above the excel cells.

Figure 6: The ERDAS Imagine model maker. 

Figure 7: The execution of equation one in
the model maker.

Figure 8: The execution of equation three in
the model maker.

Results

Figure 9 displays the surface temperature image, after it was turned into a completed map in ArcMap. It is very easy to see the temperature differences between water, concrete, and vegetation. Within ArcMap, it is possible to determine the exact kinetic temperature of each individual pixel using the identify tool.

Sources

Landsat image is from Earth Resources Observation and Science Center, United States Geological Survey.