DIRS Lab 76-3215
February 21, 2017 at 2:00am
PhD Imaging Science Thesis Defense


Advisor: Dr. John Schott






Land surface temperature (LST) is an Earth system data record that is important to many areas of study such as change detection, climate research, and smaller scale applications such as monitoring lakes and farms. LST is often derived from satellite thermal imagery to achieve adequate spatial and temporal coverage. The Landsat series of satellites are an unparalleled and attractive choice for developing an LST product, because they provide the longest running source of continuously acquired multispectral imagery. Landsat also has moderate spatial and temporal resolutions, and its sensors and data archives are well calibrated. The land surface temperature can be derived from a single Landsat thermal band if the atmosphere and surface emissivity are well known for each scene. The primary function of our algorithm is to perform atmospheric compensation on a per-pixel level, but eventually our process will be integrated with a global emissivity database to form the full LST product. 

The LST algorithm was initially limited to Landsat scenes in North America, which motivated our efforts to extend the algorithm’s operability to the entire globe. This effort allowed us to perform a thorough global validation for Landsat 7. Another portion of our work was focused on developing a method for estimating the uncertainty in the LST retrievals, so that users can make informed decisions on which pixels to use. This was accomplished by dividing the global validation data into different ranges of cloud proximity and transmission, then using the root mean square error (RMSE) for each group to help define uncertainty. When transmission was greater than 0.7 and clouds were at least 5 km away from the pixel of interest, the difference between our predictions and the observed error in LST had RMSEs of roughly 1 K. When a bias removal technique was used on the observed LST errors, the RMSEs for the same conditions were reduced to around 0.75 K. Based on these values, we are confident that our uncertainty estimation method will be a useful addition to the LST product.