Comparison of diurnal temperature cycle model and polynomial regression technique in temporal normalization of airborne land surface temperature

Airborne TIR remote sensing can obtain land surface temperature (LST) with high spatial resolution. However, the swath width of airborne stripes is usually limited. Therefore, it is necessary to generate the LSTs for a large area through temporal normalization of LSTs derived from different stripes. By selecting an agricultural oasis as the study area, this study compares the diurnal temperature cycle (DTC) model and polynomial regression (PR) technique in the temporal normalization of the LSTs derived from the Thermal Airborne Spectrographic Imager (TASI) data. The results show that the DTC model has better accuracy in normalizing the LSTs. However, the PR technique is simple and requires less ancillary data. The DTC method can normalize the LST to any specific time and generate temporally continuous LSTs, while the PR method can only do relative normalization. This study is helpful to reduce the temperature differences of different airborne stripes and obtain airborne LSTs with both high spatial and temporal resolutions.