Lab 2: Surface Temperature Extraction from Thermal Remote Sensing Data

Introduction:

Thermal imagery holds incredibly useful information, but requires a very different workflow than reflective imagery. In this lab, I first performed visual image interpretation on thermal imagery, then progressed into constructing models in order to quantitatively estimate land surface temperature.

Methods:

In order to become comfortable with thermal imagery, I first examined thermal imagery captured over the Eau Claire, Wisconsin. I used my knowledge of existing features to interpret the images' brightness values, in order to further develop my understanding of the properties of thermal images and the features they capture.

After gaining some understanding of the imagery, I created a model to convert ETM+ imagery's values from digital numbers to the satellite's original radiance values. The model used the following equation, "spectral radiance = Grescale * digital number value + Brescale". In relation to the slope equation (y=mx+b), Grescale is the value the radiance was divided by in order to create the digital number. Brescale is the lowest radiance value the satellite recorded. In order to calculate the Grescale, I used the following equation, "Grescale = (LMAX - LMIN)/(QCALMAX - QCALMIN)". The LMAX and LMIN values are the highest and lowest radiance values originally recorded by the satellite, respectively. QCALMAX and QCALMIN are the highest and lowest calibrated pixel values, respectively. As the thermal imagery is recorded in 8-bits, QCALMAX is 255 and QCALMIN is 1. After calculating the Grescale, I was able to use the Grescale and Brescale to calculate the at-satellite radiance values (Figure 1).

Figure 1: The model for calculating at-satellite radiance.

Unfortunately, radiance is only the satellite's recorded value, and not the actual surface temperature. In order to determine the actual surface temperature, I needed to implement an additional equation. The equation to convert radiance to surface temperature is, "Temperature = k2 / ln((k1/radiance)+1)". The values k1 and k2 are calibration constants for the satellite that were recorded before it was launched into orbit, so implementing the equation only required me to identify the values for k1 and k2. I created a new model, with the output radiance raster as the input value for the new equation. Running the new model generated a temperature surface raster, showing temperature in degrees Kelvin.

Next, I performed the same series of equations to calculate radiance and surface temperature, only using Landsat TM imagery instead of ETM+ imagery. Calculating surface temperature required different values for k1 and k2, as Landsat TM was calibrated slightly differently than Landsat ETM+

The final step of this lab was to calculate land surface temperature for Chippewa and Eau Claire counties from a Landsat 8 thermal image. First, I performed an image subset using an area of interest file of the counties' boundaries. Next, I followed similar procedures as the previous two temperature calculations, only using different k1 and k2 values. After generating a surface temperature raster with degrees recorded in Kelvin, I added an additional operation to convert the temperature into degrees Fahrenheit.

Results:

Each of the calculations produced a useful output, but by converting the temperature from Kelvin into Fahrenheit, the usability of the Landsat 8 output image was greatly increased (Figure 2).

Figure 2: Fahrenheit surface temperature extracted from Landsat 8's thermal band.

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.